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Tech Data Reports First Quarter Fiscal Year 2020 Results

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CLEARWATER, Fla.–(BUSINESS WIRE)–Tech Data (NASDAQ: TECD) (the “Company”) today announced its financial
results for the first quarter ended April 30, 2019.

    First quarter ended April 30,
($ in millions,

except per share amounts)

  2019   2018  

Y/Y
Change

Net Sales   $8,406.4   $8,548.3   -2%
 
Gross profit $509.4 $523.1 -3%
Gross margin 6.06% 6.12% -6 bps
 
SG&A expenses (GAAP) $405.8 $422.4 -4%
% of net sales 4.83% 4.94% -11 bps
 
SG&A expenses (Non-GAAP) $384.6 $399.1 -4%
% of net sales 4.58% 4.67% -9 bps
 
Operating income (GAAP) $97.6 $70.5 38%
Operating margin (GAAP) 1.16% 0.82% 34 bps
 
Operating income (Non-GAAP) $124.8 $124.1 1%
Operating margin (Non-GAAP) 1.48% 1.45% 3 bps
 
Net income (GAAP) $55.4 $33.7 64%
Net income (Non-GAAP) $75.9 $70.8 7%
 
EPS – diluted (GAAP) $1.49 $0.87 71%
EPS – diluted (Non-GAAP)   $2.04   $1.84   11%

A reconciliation of GAAP to non-GAAP financial measures is presented in
the financial tables of this press release.
This information is
also available on the Investor Relations section of Tech Data’s website
at www.techdata.com/investor.

“We are pleased to report a solid start to Tech Data’s fiscal year 20.
In Q1 we delivered double-digit earnings per share growth, generated
positive cash flow and earned an industry-leading return on invested
capital – all while making good progress on our strategy and continuing
to invest for the future. Our worldwide teams executed well in the
quarter, despite market uncertainty,” said Rich Hume, chief executive
officer. “Looking ahead, although IT market growth has slowed somewhat
from the year-ago levels, demand continues to be solid, and we remain
positive on the overall IT spending outlook.”

Regional Financial Highlights for the First Quarter Ended April 30,
2019:

    First quarter ended April 30,
($ in millions)   2019   2018  

Y/Y
Change

AMERICAS

     
Net Sales $3,789.2 $3,618.2 5%
% of WW net sales 45% 42%
 
Operating income (GAAP) $68.6 $61.3 12%
% of net sales 1.81% 1.70% 11 bps
 
Operating income (Non-GAAP) $84.7 $85.9 -1%
% of net sales   2.24%   2.38%   -14 bps

EUROPE

Net Sales $4,309.5 $4,661.7 -8%
% of WW net sales 51% 55%
 
Operating income (GAAP) $36.4 $17.3 110%
% of net sales 0.85% 0.37% 48 bps
 
Operating income (Non-GAAP) $45.6 $43.6 4%
% of net sales   1.06%   0.94%   12 bps

ASIA PACIFIC

Net Sales $307.7 $268.4 15%
% of WW net sales 4% 3%
 
Operating income (loss) (GAAP) $0.9 ($0.6) NM
% of net sales 0.28% -0.21% 49 bps
 
Operating income (Non-GAAP) $2.8 $1.1 161%
% of net sales   0.91%   0.40%   51 bps

Note: NM = not meaningful, WW = worldwide
Stock-based compensation
expense was $8.3 million, an increase of $0.7 million, compared to the
prior-year quarter. These expenses are excluded from the regional
operating results and presented as a separate line item in the company’s
segment reporting (see the GAAP to non-GAAP reconciliation in the
financial tables of this press release).

  • Net sales were $8.4 billion, a decrease of 2 percent compared to the
    prior-year quarter. On a constant currency basis, net sales increased
    3 percent.

    • Americas: Net sales were $3.8 billion, an increase of 5 percent
      compared to the prior-year quarter. On a constant currency basis,
      net sales increased 6 percent.
    • Europe: Net sales were $4.3 billion, a decrease of 8 percent
      compared to the prior-year quarter. On a constant currency basis,
      net sales increased 1 percent.
    • Asia Pacific: Net sales were $0.3 billion, an increase of 15
      percent compared to the prior-year quarter. On a constant currency
      basis, net sales increased 19 percent.
  • Net cash generated by operations during the quarter was $63 million.
  • Return on invested capital for the trailing twelve months was 13
    percent, compared to 4 percent in the prior year. Adjusted return on
    invested capital for the trailing twelve months was 14 percent,
    compared to 11 percent in the prior year.

“During Q1, we generated $63 million in cash from operations, returned
$36 million to our shareholders through share repurchases, and for the
trailing twelve-month period, earned an adjusted return on invested
capital of 14 percent. In addition, we recently improved our liquidity
profile to enhance our financial strength and flexibility – all of which
reflect our disciplined approach to optimizing our business and
commitment to creating shareholder value,” said Chuck Dannewitz,
executive vice president, chief financial officer.

Business Outlook

  • For the quarter ending July 31, 2019, the Company anticipates:

    • Worldwide net sales to be in the range of $8.6 billion to $8.9
      billion
    • EPS to be in the range of $1.53 to $1.83 and non-GAAP EPS to be in
      the range of $2.15 to $2.45
    • An effective tax rate in the range of 24 percent to 26 percent
  • This guidance assumes an average U.S. dollar to euro exchange rate of
    $1.12 to €1.00 which compares to $1.17 to €1.00 in the year-ago period.

Webcast Details

Tech Data will hold a conference call today at 9:00 a.m. (ET) to discuss
its financial results for the first quarter ended April 30, 2019. A
webcast of the call, including supplemental schedules, will be available
to all interested parties and can be obtained at www.techdata.com/investor.
The webcast will be available for replay for three months.

Non-GAAP Financial Information

The non-GAAP financial information contained in this release is included
with the intention of providing investors a more complete understanding
of the Company’s operational results and trends, but should only be used
in conjunction with results reported in accordance with Generally
Accepted Accounting Principles (“GAAP”). Certain non-GAAP measures
presented in this release or other releases, presentations and similar
documents issued by the Company include sales, income or expense items
as adjusted for the impact of changes in foreign currencies (referred to
as “constant currency”), non-GAAP operating income, non-GAAP operating
margin, non-GAAP net income, non-GAAP earnings per diluted share and
Adjusted Return on Invested Capital. Certain non-GAAP measures also
exclude acquisition-related intangible assets amortization expense,
benefits associated with legal settlements, acquisition, integration and
restructuring expenses, value-added tax assessments and related interest
expense, tax indemnifications and changes in deferred tax valuation
allowances. A detailed reconciliation of the adjustments between results
calculated using GAAP and non-GAAP in this release is contained in the
attached financial schedules. This information can also be obtained from
the Company’s Investor Relations website at www.techdata.com/investor.

Forward-Looking Statements

Certain statements in this communication may contain “forward-looking
statements” within the meaning of the Private Securities Litigation
Reform Act of 1995. These statements, including statements regarding
Tech Data’s plans, objectives, expectations and intentions, Tech Data’s
financial results and estimates and/or business prospects, involve a
number of risks and uncertainties and actual results could differ
materially from those projected. These forward looking statements are
based on current expectations, estimates, forecasts, and projections
about the operating environment, economies and markets in which Tech
Data operates and the beliefs and assumptions of our management. Words
such as “expects,” “anticipates,” “targets,” “goals,” “projects,”
“intends,” “plans,” “believes,” “seeks,” “estimates,” variations of such
words, and similar expressions are intended to identify such forward
looking statements. In addition, any statements that refer to
projections of Tech Data’s future financial performance, our anticipated
growth and trends in our businesses, and other characterizations of
future events or circumstances, are forward looking statements. These
forward looking statements are only predictions and are subject to
risks, uncertainties, and assumptions. Therefore, actual results may
differ materially and adversely from those expressed in any forward
looking statements.

For additional information with respect to risks and other factors which
could occur, see Tech Data’s Annual Report on Form 10-K for the year
ended January 31, 2019, including Part I, Item 1A, “Risk Factors”
therein, Quarterly Reports on Form 10-Q, Current Reports on Form 8-K and
other securities filings with the Securities and Exchange Commission
(the “SEC”) that are available at the SEC’s website at www.sec.gov
and other securities regulators. Readers are cautioned not to place
undue reliance upon any such forward-looking statements, which speak
only as of the date made. Many of these factors are beyond Tech Data’s
control. Unless otherwise required by applicable securities laws, Tech
Data disclaims any intention or obligation to update or revise any
forward-looking statements, whether as a result of new information,
future events or otherwise. Tech Data undertakes no duty to update any
forward looking statements contained herein to reflect actual results or
changes in Tech Data’s expectations.

About Tech Data

Tech Data connects the world with the power of technology. Our
end-to-end portfolio of products, services and solutions, highly
specialized skills, and expertise in next-generation technologies enable
channel partners to bring to market the products and solutions the world
needs to connect, grow and advance. Tech Data is ranked No. 88 on the
Fortune 500® and has been named one of Fortune’s “World’s
Most Admired Companies” for 10 straight years. To find out more, visit www.techdata.com or
follow us on TwitterLinkedIn,
and Facebook.

TECH DATA CORPORATION AND SUBSIDIARIES
CONSOLIDATED STATEMENT OF OPERATIONS
(In thousands, except per share amounts)
(Unaudited)
 
Three months ended April 30,
2019   2018
Net sales $ 8,406,424 $ 8,548,319
Cost of products sold   7,897,045   8,025,202
Gross profit 509,379 523,117
Operating expenses:
Selling, general and administrative expenses 405,816 422,361
Acquisition, integration, and restructuring expenses 6,221 33,225
Legal settlements and other, net   (282)   (2,965)
  411,755   452,621
Operating income 97,624 70,496
Interest expense 26,257 25,922
Other (income) expense, net   (693)   1,917
Income before income taxes 72,060 42,657
Provision for income taxes   16,660   8,958
Net income $ 55,400 $ 33,699
 
Earnings per share:
Basic $ 1.50 $ 0.88
Diluted $ 1.49 $ 0.87
Weighted average common shares outstanding:
Basic   37,011   38,281
Diluted   37,247   38,561
TECH DATA CORPORATION AND SUBSIDIARIES
CONSOLIDATED BALANCE SHEET
(In thousands, except par value and share amounts)
(Unaudited)
   
April 30, January 31,
2019 2019
ASSETS    
 
Current assets:
Cash and cash equivalents $ 797,500 $ 799,123
Accounts receivable, net 5,423,370 6,241,740
Inventories 3,260,840 3,297,385
Prepaid expenses and other assets   367,858   354,601
Total current assets 9,849,568 10,692,849
Property and equipment, net 271,906 274,917
Goodwill 887,175 892,990
Intangible assets, net 924,338 950,858
Other assets, net   378,762   174,938
Total assets $ 12,311,749 $ 12,986,552
 
 
LIABILITIES AND SHAREHOLDERS’ EQUITY
Current liabilities:
Accounts payable $ 6,715,555 $ 7,496,466
Accrued expenses and other liabilities 984,366 1,000,126
Revolving credit loans and current maturities of long-term debt, net   123,092   110,368
Total current liabilities 7,823,013 8,606,960
Long-term debt, less current maturities 1,297,943 1,300,554
Other long-term liabilities   274,887   142,315
Total liabilities $ 9,395,843 $ 10,049,829
 
Shareholders’ equity:
Common stock, par value $0.0015; 200,000,000 shares authorized;
59,245,585
$ 89 $ 89
shares issued at April 30, 2019 and January 31, 2019
Additional paid-in capital 836,508 844,206
Treasury stock, at cost (22,483,529 and 22,305,464 shares at April
30, 2019
and January 31, 2019) (1,065,657) (1,037,872)
Retained earnings 3,141,914 3,086,514
Accumulated other comprehensive income   3,052   43,786
Total shareholders’ equity   2,915,906   2,936,723
Total liabilities and shareholders’ equity $ 12,311,749 $ 12,986,552
TECH DATA CORPORATION AND SUBSIDIARIES
CONSOLIDATED STATEMENT OF CASH FLOWS
(In thousands)
(Unaudited)
 
Three months ended April 30,
2019   2018
Cash flows from operating activities:  
Cash received from customers $ 11,913,347 $ 11,514,374
Cash paid to vendors and employees (11,800,318 ) (12,038,399 )
Interest paid, net (35,101 ) (33,763 )
Income taxes paid (14,739 ) (8,830 )
Net cash provided by (used in) operating activities 63,189   (566,618 )
Cash flows from investing activities:
Expenditures for property and equipment (7,745 ) (4,894 )
Software and software development costs (7,534 ) (3,561 )
Other (548 ) (267 )
Net cash used in investing activities (15,827 ) (8,722 )
Cash flows from financing activities:
Principal payments on long-term debt (5,224 ) (2,899 )
Cash paid for debt issuance costs (1,028 )
Net borrowings (repayments) on revolving credit loans 14,227 (13,291 )
Payments for employee tax withholdings on equity awards (8,602 ) (6,255 )
Proceeds from the reissuance of treasury stock 495 442
Repurchases of common stock (35,681 )  

Net cash used in financing activities

(35,813 ) (22,003 )
Effect of exchange rate changes on cash and cash equivalents (13,172 ) (12,708 )
Net decrease in cash and cash equivalents (1,623 ) (610,051 )
Cash and cash equivalents at beginning of year 799,123   955,628  
Cash and cash equivalents at end of period $ 797,500   $ 345,577  
Reconciliation of net income to net cash provided by operating
activities:
Net income $ 55,400 $ 33,699
Adjustments to reconcile net income to net cash provided by (used
in) operating activities:

Depreciation and amortization

37,257 40,481
Provision for losses on accounts receivable 1,765 924
Stock-based compensation expense 8,305 7,587
Accretion of debt discount and debt issuance costs 378 378
Changes in operating assets and liabilities:
Accounts receivable 751,836 670,528
Inventories 2,450 (7,387 )
Prepaid expenses and other assets 2,245 (30,344 )
Accounts payable (706,381 ) (1,132,019 )
Accrued expenses and other liabilities (90,066 ) (150,465 )
Total adjustments 7,789   (600,317 )
Net cash provided by (used in) operating activities $ 63,189   $ (566,618 )
TECH DATA CORPORATION AND SUBSIDIARIES
GAAP TO NON-GAAP RECONCILIATION
(In thousands)
     
Three months ended April 30, 2019
Americas (1) Europe (1)   Asia Pacific (1)  

Stock
Compensation
Expense

Consolidated
Net Sales $ 3,789,198 $ 4,309,500 $ 307,726   $ 8,406,424
Operating income (GAAP) (1) $ 68,633 $ 36,420 $ 876 $ (8,305) $ 97,624
Acquisition, integration and restructuring expenses 2,911 3,024 286 6,221
Legal settlements and other, net (282) (282)
Tax indemnifications 320 320
Acquisition-related intangible assets amortization expense 13,440 6,115 1,324   20,879
Total non-GAAP operating income adjustments $ 16,069 $ 9,139 $ 1,930 $ – $ 27,138
Operating income (non-GAAP) $ 84,702 $ 45,559 $ 2,806 $ (8,305) $ 124,762
Operating margin (GAAP) 1.81% 0.85% 0.28% 1.16%
Operating margin (non-GAAP) 2.24% 1.06% 0.91% 1.48%
 
(1) GAAP operating income does not include stock
compensation expense at the regional level.
                 
Three months ended April 30, 2018
Americas (1) Europe (1) Asia Pacific (1)

Stock
Compensation
Expense

Consolidated
Net Sales $ 3,618,206 $ 4,661,702 $ 268,411   $ 8,548,319
Operating income (loss) (GAAP) (1) $ 61,342 $ 17,318 $ (577) $ (7,587) $ 70,496
Acquisition, integration and restructuring expenses 13,916 17,988 321 1,000 33,225
Legal settlements and other, net (2,965) (2,965)
Acquisition-related intangible assets amortization expense 13,643 8,329 1,332   23,304
Total non-GAAP operating income adjustments $ 24,594 $ 26,317 $ 1,653 $ 1,000 $ 53,564
Operating income (non-GAAP) $ 85,936 $ 43,635 $ 1,076 $ (6,587) $ 124,060
Operating margin (GAAP) 1.70% 0.37% -0.21% 0.82%
Operating margin (non-GAAP) 2.38% 0.94% 0.40% 1.45%
 
(1) GAAP operating income does not include stock
compensation expense at the regional level.
TECH DATA CORPORATION AND SUBSIDIARIES
GAAP TO NON-GAAP RECONCILIATION
(In thousands)
 
Selling, general and administrative expenses (“SG&A”) Three months ended April 30,
2019   2018
Net Sales $ 8,406,424 $ 8,548,319
SG&A Expenses (GAAP) $ 405,816 $ 422,361
Tax indemnifications (320)
Acquisition-related intangible assets amortization expense   (20,879)   (23,304)
SG&A Expenses (non-GAAP) $ 384,617 $ 399,057
 
SG&A Expenses (GAAP) % 4.83% 4.94%
SG&A Expenses (non-GAAP) % 4.58% 4.67%
  Three months ended April 30,
2019       2018    
Net Income   Diluted EPS Net Income   Diluted EPS
GAAP Results $55,400 $1.49 $33,699 $0.87
Acquisition, integration and restructuring expenses 6,221 0.17 33,225 0.86
Legal settlements and other, net (282) (0.01) (2,965) (0.08)
Value added tax assessments and related interest expense (928) (0.02)
Tax indemnifications 320 0.01
Acquisition-related intangible assets amortization expense 20,879 0.56 23,304 0.61
Income tax effect of tax indemnifications (320) (0.01)
Income tax effect of other adjustments above (6,321) (0.17) (12,908) (0.33)
Change in deferred tax valuation allowances (2,600) (0.07)
           
Non-GAAP Results $75,897   $2.04 $70,827   $1.84

Return on Invested Capital (ROIC)

 
 
Twelve months ended April 30,
TTM Net Operating Profit After Tax (NOPAT)*: 2019   2018
Operating income $ 520,930 $ 405,497
Income taxes on operating income (1)   (52,272)   (242,229)
NOPAT $ 468,658 $ 163,268
 
Average Invested Capital:
Short-term debt (5-qtr end average) $ 115,018 $ 262,413
Long-term debt (5-qtr end average) 1,361,506 1,683,828
Shareholders’ Equity (5-qtr end average)   2,881,968   2,745,501
Total average capital 4,358,492 4,691,742
Less: Cash (5-qtr end average)   (676,308)   (751,732)
Average invested capital less average cash $ 3,682,184 $ 3,940,010
ROIC 13% 4%
 
* Trailing Twelve Months is abbreviated as TTM.
(1) Income taxes on operating income was calculated using
the trailing twelve months effective tax rate.

Adjusted Return on Invested Capital (ROIC)

Twelve months ended April 30,
TTM Net Operating Profit After Tax (NOPAT), as adjusted*: 2019   2018
Non-GAAP operating income (1) $ 708,588 $ 603,559
Income taxes on non-GAAP operating income (2) (179,283) (178,518)
NOPAT, as adjusted $ 529,305 $ 425,041
 
Average Invested Capital, as adjusted:
Short-term debt (5-qtr end average) $ 115,018 $ 262,413
Long-term debt (5-qtr end average) 1,361,506 1,683,828
Shareholders’ Equity (5-qtr end average) 2,881,968 2,745,501
Tax effected impact of non-GAAP adjustments (3) 44,860 95,713
Total average capital, as adjusted 4,403,352 4,787,455
Less: Cash (5-qtr end average) (676,308) (751,732)
Average invested capital less average cash $ 3,727,044 $ 4,035,723
Adjusted ROIC 14% 11%
*   Trailing Twelve Months is abbreviated as TTM.

(1)

Represents operating income as adjusted to exclude acquisition,
integration and restructuring expenses, legal settlements and other,
net, gain on disposal of subsidiary, value added tax assessments,
acquisition-related intangible assets amortization expense, goodwill
impairment and tax indemnifications.

(2)

Income taxes on non-GAAP operating income was calculated using the
trailing twelve months effective tax rate adjusted for the impact of
non-GAAP adjustments during the respective periods.

(3)

Represents the 5 quarter average of the year-to-date impact of
non-GAAP adjustments.

Guidance Reconciliation

 
 
Three months ending July 31, 2019

Low end of
guidance range

High end of
guidance range

Earnings per share – diluted $1.53 $1.83
Acquisition, integration and restructuring expenses 0.59 0.59
Acquisition-related amortization of intangibles 0.24 0.24
Income tax effect of the above adjustments (0.21) (0.21)
Non-GAAP earnings per share – diluted $2.15 $2.45

Contacts

Investor Contact
Tania Almond
Investor Relations
Director
+1 727.538.7064
tania.almond@techdata.com

Media Contact
Bobby Eagle
Director, External
Communications
+1 727.538.5864
bobby.eagle@techdata.com

Cannabis

Strainprint™ Technologies Welcomes Organic Medical Cannabis Producer to Growing List of Subscribers

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Strainprint™ Technologies Ltd, the leader in cannabis data and analytics is pleased to expand their nation-wide coverage with the addition of Stewart Farms.

Stewart Farms is a late-stage applicant headquartered in Alberta that is building a 100,000 sq. ft. vertical aquaponics farm in Saint StephensNew Brunswick. They will be utilizing automation, vertical farming, and land-based aquaculture to produce medical grade organic cannabis for both recreational and medical markets in Canada. All of their products will be free of herbicides, pesticides and synthetic nutrients. At full capacity, they will reach more than 10,000 kgs per year of organically farmed cannabis and more than 200,000 kgs of organic tilapia.

Stewart Farms’ long term vision is to deliver tools and products to support  future customers and patients during their health and wellness journeys. “Through our partnership with Strainprint we gain direct access to their incomparable data and analytics tools. This will aid us in educating a wider audience on the medical benefits and best practices of cannabis-based medicine,” said Tanner Stewart, Co-founder & CEO of Stewart Farms. “We know our customers are looking to consume more than dried cannabis. We know Canadians of all legal ages are trying to sort through their personal engagement with cannabis as a medicine. What we want to know, on an ongoing basis, is what is and is not working for people. Strainprint’s patient-led data services will give us the insight needed to create custom products and further understand the benefits of our existing products. Finally, our goal is to partner with companies and teams that truly have people’s best interest at heart. Caring about a patient’s success is what makes up the core of the Strainprint team. We couldn’t be happier to move forward with them at our side.”

Stewart Farms will access the Strainprint Analytics web platform, the most sophisticated cannabis analytics platform available to improve product development. Strainprint Analytics is built on top of the largest and most granular scientific data set of its kind in the world, with more than 1.3 million anonymized, patient reported medical cannabis outcomes and more than 65 million data points on strain efficacy.

“We’re thrilled to provide our real-time, crowd sourced data to an environmentally conscious company that uses innovative, cutting edge farming technology to produce top-tier medical cannabis,” said Strainprint CEO, Andrew Muroff. “Our organizations are equally committed to improving lives using cannabis therapy, and our shared core motivation is to provide guidance and support to help cannabis patients achieve their health goals.”

 

SOURCE Strainprint Technologies Ltd.

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Field Trip Ventures Inc. Retains KCSA Strategic Communications as Public Relations Counsel

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Greenery Map, the world’s first and only cannabis search engine to allow users to search for cannabis products based on their desired mood, medicinal use, and method of consumption, is now offering users – both businesses and consumers – the opportunity to register and connect to the app in an entirely mobile way. Previously, businesses were required to access GreeneryMap.com from a desktop in order to manage accounts and connect to the inventory API system, but that road block has been removed, allowing for an entirely mobile experience through the Greenery Map app.

App users turn to Greenery Map to help them decide on the strain of Cannabis and product that is right for them depending on their desired mood or medicinal effect. Following the matching, consumers are given a detailed history and description of the strain so that they can make educated purchases, cutting down on time that budtenders need to spend with patrons and speeding up the purchase process. Consumers can then see all of the ways to purchase said product, including the nearby dispensaries that have it in stock, online options, and even delivery options through the app. Greenery Map fully integrates with a dispensary’s inventory API system to update in real-time and give consumers real-time information on dispensary stock for the item they are looking for. The Greenery Map system also provides cannabis businesses in both the B2B and B2C sectors a private database to share and sell information and products. For Cannabis businesses, Greenery Map also offers the opportunity to open bank and merchant accounts through their partnership with PayHouse Consulting Group.

 

SOURCE Greenery Map

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WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019

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Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?

WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.

President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.

This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.

Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML

Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.

A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.

Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.

“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.

Major Figure Panelists Discuss the Way Ahead for FML

The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.

Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?

List of Award-Winners

Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.

Best Theory Paper Award: 
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;

Best Application Paper Award: 
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang ChenJindong WangChaohui YuWen Gao & Xin Qin;

Best Student Paper Award: 
Quantifying the Performance of Federated Transfer Learning. By Qinghe JingWeiyan Wang, Junxue Zhang, Han Tian & Kai Chen;

Best Presentation Award: 
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.

President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard TongandVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.

“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.

Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.

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