International Workshop on Trustworthy Federated Learning
in Conjunction with IJCAI 2022 (FL-IJCAI'22)


Submission Due: May 23, 2022 (23:59:59 AoE)
Notification Due: June 10, 2022 (23:59:59 AoE)
Final Version Due: June 20, 2022 (23:59:59 AoE)

Workshop Date (In-Person Program): Saturday, July 23, 2022 (09:00 – 12:50, UTC+2)
Venue: Room Lehar 1, Messe Wien Exhibition and Congress Center, Vienna, Austria

Workshop Date (Online Program): ()
Venue: Zoom

Workshop Program Video Recording

In-Person Workshop Program (On Saturday, July 23, 2022 in Vienna)

  
Vienna Time
(UTC+2)
Activity
  
09:00 – 09:15 Opening Remarks
09:15 – 09:45 Special Invited Talk by the Sponsor: Social, Secure, Scalable, and Efficient Federated Learning, by Salman Avestimehr
09:45 – 10:45 Oral Presentation Session 1 (10 min per talk + 5 min Q&A each)
  1. Best Paper Award: Yann Fraboni, Richard Vidal, Laetitia Kameni and Marco Lorenzi. A General Theory for Client Sampling in Federated Learning
  2. Best Student Paper Award: Sihun Baek, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis and Seong-Lyun Kim. Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
  3. Haley Hoech, Roman Rischke, Karsten Müller and Wojciech Samek. FedAUXfdp: Differentially Private One-Shot Federated Distillation
  4. Jia Liu and Yaochu Jin. Bi-fidelity Multi-objective Neural Architecture Search for Adversarial Robustness with Surrogate as a Helper-objective
10:45 – 11:15 Coffee Break
11:15 – 11:45 Invited Talk 1: Privacy-Preserving Bayesian Evolutionary Optimization, by Yaochu Jin
11:45 – 12:45 Oral Presentation Session 2 (10 min per talk + 5 min Q&A each)
  1. Ljubomir Rokvic, Panayiotis Danassis and Boi Faltings. Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation
  2. Martin Isaksson, Edvin Listo Zec, Rickard Cöster, Daniel Gillblad and Sarunas Girdzijauskas. Adaptive Expert Models for Personalization in Federated Learning
  3. Edvin Listo Zec, Ebba Ekblom, Martin Willbo, Olof Mogren and Sarunas Girdzijauskas. Decentralized adaptive clustering of deep nets is beneficial for client collaboration
  4. Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic and Gerald Quentin Maguire Jr. Fast Server Learning Rate Tuning for Coded Federated Dropout
12:45 – 12:50 Award Ceremony & Closing Remarks
   

Online Workshop Program (On via Zoom)

  
Vienna Time
(UTC+2)
Your Local Time
(
)
Activity
  
09:00 – 09:05 Opening Remarks
09:05 – 09:35 Distinguish Keynote Lecture: Recent Advances in Trustworthy Federated Learning (Video), by Qiang Yang
09:35 – 10:05 Invited Talk 2: NVIDIA FLARE for Federated Learning in Healthcare, by Yongnan Ji
10:05 – 10:35 Invited Talk 3: Pratice in Privacy and Security of Federated Learning from China Telecom, by Zuping Wu
10:35 – 12:35 Oral Presentation Session 3 (Session Chair: Guodong Long) (10 min per talk + 5 min Q&A each)
  1. Innovation Award: Yiqiang Chen, Wang Lu, Xin Qin, Jindong Wang and Xing Xie. MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
  2. Innovation Award: Shubao Zhao, Jia Liu, Guoliang Ma, Jie Yang, Di Liu and Zengxiang Li. Cluster-driven Personalized Federated Learning for Natural Gas Load Forecasting
  3. Haoran Li, Ying Su, Qi Hu, Jiaxin Bai, Yilun Jin and Yangqiu Song. FedAssistant: Dialog Agents with Two-side Modeling
  4. Xueyang Wu, Shengqi Tan, Qian Xu and Qiang Yang. WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
  5. Liu Yang, Junxue Zhang, Di Chai, Kai Chen and Qiang Yang. Practical and Secure Federated Recommendation with Personalized Mask
  6. Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin and Kai Chen. Secure Forward Aggregation for Vertical Federated Neural Networks
  7. Wenxin Wang, Jianyi Zhang, Zhi Sun, Zixiao Xiang and Yuyang Han. RRCM: A Fairness Framework for Federated Learning
  8. Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan and Qiang Yang. An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
12:35 – 13:40 Lunch/Dinner Break
13:40 – 14:10 Invited Talk 4: Building Ecosystem of Federated Learning - Opensource, Standards, Blockchain and Beyond, by Victoria Wang
14:10 – 14:40 Invited Talk 5: Towards Collaborative Learning - Personalization and Byzantine Robust Training, by Martin Jaggi
14:40 – 16:55 Oral Presentation Session 4 (Session Chair: Sin G. Teo) (10 min per talk + 5 min Q&A each)
  1. Wenjie Li, Qiaolin Xia, Hao Cheng, Junfeng Deng, Jiangming Liu, Kouying Xue, Yong Cheng and Shu-Tao Xia. Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfer
  2. Jianping Cai, Yang Liu, Ximeng Liu, Jiayin Li and Hongbin Zhuang. Privacy-Preserving Federated Cross-Domain Social Recommendation
  3. Wei Yu. TEE based Cross-silo Trustworthy Federated Learning Infrastructure
  4. Mohsin Hasan, Zehao Zhang, Kaiyang Guo, Mahdi Karami, Guojun Zhang, Xi Chen and Pascal Poupart. Robust One Round Federated Learning with Predictive Space Bayesian Inference
  5. Leye Wang, Chongru Huang and Xiao Han. Vertical Federated Knowledge Transfer via Representation Distillation
  6. Zijian Li, Jiawei Shao, Yuyi Mao, Jessie Hui Wang and Jun Zhang. Federated Learning with GAN-based Data Synthesis for Non-IID Clients
  7. Ahmed Elkordy, Yahya H. Ezzeldin and Salman Avestimehr. Secure Federated Analytics for Set Intersection
  8. Georgios Kollias, Theodoros Salonidis and Shiqiang Wang. Sketch to Skip and Select: Communication Efficient Federated Learning using Locality Sensitive Hashing
  9. Zihan Chen, Songshang Liu, Hualiang Wang, Howard H. Yang, Tony Quek and Zuozhu Liu. Towards Federated Long-Tailed Learning
16:55 – 17:00 Award Ceremony & Closing Remarks
   

Distinguished Keynote Lecture

   

Title: Recent Advances in Trustworthy Federated Learning (Video)

Speaker: Qiang Yang, Chief AI Officer (CAIO), WeBank / Chair Professor, Hong Kong University of Science and Technology (HKUST)

Biography
Qiang Yang is the head of the AI Department at WeBank (Chief AI Officer) and Chair Professor at the Computer Science and Engineering (CSE) Department of the Hong Kong University of Science and Technology (HKUST), where he was a former head of CSE Department and founding director of the Big Data Institute (2015-2018). His research interests include AI, machine learning, and data mining, especially in transfer learning, automated planning, federated learning, and case-based reasoning. He is a fellow of several international societies, including ACM, AAAI, IEEE, IAPR, and AAAS. He received his Ph.D. in Computer Science in 1989 and his M.Sc. in Astrophysics in 1985, both from the University of Maryland, College Park. He obtained his B.Sc. in Astrophysics from Peking University in 1982. He had been a faculty member at the University of Waterloo (1989-1995) and Simon Fraser University (1995-2001). He was the founding Editor-in-Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and IEEE Transactions on Big Data (IEEE TBD). He served as the President of International Joint Conference on AI (IJCAI, 2017-2019) and an executive council member of Association for the Advancement of AI (AAAI, 2016-2020). Qiang Yang is a recipient of several awards, including the 2004/2005 ACM KDDCUP Championship, the ACM SIGKDD Distinguished Service Award (2017), and AAAI Innovative Application Awards (2018, 2020 and 2022). He was the founding director of Huawei's Noah's Ark Lab (2012-2014) and a co-founder of 4Paradigm Corp, an AI platform company. He is an author of several books including Intelligent Planning (Springer), Crafting Your Research Future (Morgan & Claypool), and Constraint-based Design Recovery for Software Engineering (Springer).


Special Invited Talk by the Sponsor

   

Title: Social, Secure, Scalable, and Efficient Federated Learning

Speaker: Salman Avestimehr, FedML / University of Southern California (USC), USA

Biography
Salman Avestimehr is a Dean's Professor, the inaugural director of the USC-Amazon Center on Secure and Trusted Machine Learning (Trusted AI), and the director of the Information Theory and Machine Learning (vITAL) research lab at the Electricaland Computer Engineering Department of University of Southern California. He is also an Amazon Scholar at Alexa AI. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science, both from the University of California,Berkeley. Prior to that, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003. His research interests include information theory and coding theory, and large-scale distributed computing and machine learning, secure andprivate computing, and blockchain systems.

Dr. Avestimehr has received a number of awards for his research, including the James L. Massey Research & Teaching Award from IEEE Information Theory Society, an Information Theory Society and Communication Society Joint Paper Award, a Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House (President Obama), a Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research, a National Science Foundation CAREER award, the David J. Sakrison MemorialPrize, and several Best Paper Awards at Conferences. He has been an Associate Editor for IEEE Transactions on Information Theory and a general Co-Chair of the 2020 International Symposium on Information Theory (ISIT). He is a Fellow of IEEE.


Invited Talks

   

Title: Privacy-Preserving Bayesian Evolutionary Optimization

Speaker: Yaochu Jin, Alexander von Humboldt Professor, Bielefeld University, Germany

Biography
Yaochu Jin is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany. He is also a Surrey Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a "Finland Distinguished Professor" of University of Jyväskylä, Finland, "Changjiang Distinguished Visiting Professor", Northeastern University, China, and "Distinguished Visiting Scholar", University of Technology Sydney, Australia. His main research interests include evolutionary optimization and learning, trustworthy machine learning and optimization, and evolutionary developmental AI. Prof Jin is presently the Editor-in-Chief of Complex & Intelligent Systems. He was the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, an IEEE Distinguished Lecturer in 2013-2015 and 2017-2019, the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2015-2016). He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Web of Science as "a Highly Cited Researcher" consecutively from 2019 to 2021. He is a Member of Academia Europaea and Fellow of IEEE.

   

Title: NVIDIA FLARE for Federated Learning in Healthcare

Speaker: Yongnan Ji, NVIDIA, China

Biography
Yongnan Ji is the NVIDIA Healthcare Ecosystem Manager in China, supporting NVIDIA Healthcare ecosystem with NVIDIA's latest technology. As an expert in medical imaging and artificial intelligence, he has published core patents and academic papers covering areas like medical imaging, image analysis and image AI. Dr Ji graduated from University of Nottingham, UK. He previously worked at GE Heatlchare, Toshiba Medical and Samsung Advanced Research Institute.

   

Title: Pratice in Privacy and Security of Federated Learning from China Telecom

Speaker: Zuping Wu, China Telecom, China

Biography
Zuping Wu is from China Telecom Research Institute, and her research focuses on Federated machine learning, AI mobile device and NR communication. She has been involved in developing several AI standards in IEEE and followed up the related issuses in 3GPP standard aasociation. She is also the Chair of IEEE SPFML-WG and IEEE C/AISC/FTFML.

   

Title: Building Ecosystem of Federated Learning - Opensource, Standards, Blockchain and Beyond

Speaker: Victoria Wang, CXO & China Strategy Lead, IEEE SA

Biography
Dr. Victoria Wang is CXO of IEEE SA. In this position, she engages global technology community and enables them to use technology standards for the benefit of humanity, particularly, for its sustainable development goals. She advised a range of technology standards and in ecosystem building, including IEEE’s standardization of federated learning. Dr. Victoria Wang is also IEEE Standard Association’s China Strategy Lead.

   

Title: Towards Collaborative Learning - Personalization and Byzantine Robust Training

Speaker: Martin Jaggi, EPFL, Switzerland

Biography
Martin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.


Awards


Accepted Papers

  1. Yann Fraboni, Richard Vidal, Laetitia Kameni and Marco Lorenzi. A General Theory for Client Sampling in Federated Learning
  2. Sihun Baek, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis and Seong-Lyun Kim. Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
  3. Haley Hoech, Roman Rischke, Karsten Müller and Wojciech Samek. FedAUXfdp: Differentially Private One-Shot Federated Distillation
  4. Yiqiang Chen, Wang Lu, Xin Qin, Jindong Wang and Xing Xie. MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
  5. Shubao Zhao, Jia Liu, Guoliang Ma, Jie Yang, Di Liu and Zengxiang Li. Cluster-driven Personalized Federated Learning for Natural Gas Load Forecasting
  6. Wenjie Li, Qiaolin Xia, Hao Cheng, Junfeng Deng, Jiangming Liu, Kouying Xue, Yong Cheng and Shu-Tao Xia. Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfer
  7. Xueyang Wu, Shengqi Tan, Qian Xu and Qiang Yang. WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
  8. Jianping Cai, Yang Liu, Ximeng Liu, Jiayin Li and Hongbin Zhuang. Privacy-Preserving Federated Cross-Domain Social Recommendation
  9. Zihan Chen, Songshang Liu, Hualiang Wang, Howard H. Yang, Tony Quek and Zuozhu Liu. Towards Federated Long-Tailed Learning
  10. Ljubomir Rokvic, Panayiotis Danassis and Boi Faltings. Privacy-preserving Data Filtering in Federated Learning Using Influence Approximation
  11. Haoran Li, Ying Su, Qi Hu, Jiaxin Bai, Yilun Jin and Yangqiu Song. FedAssistant: Dialog Agents with Two-side Modeling
  12. Jia Liu and Yaochu Jin. Bi-fidelity Multi-objective Neural Architecture Search for Adversarial Robustness with Surrogate as a Helper-objective
  13. Mohsin Hasan, Zehao Zhang, Kaiyang Guo, Mahdi Karami, Guojun Zhang, Xi Chen and Pascal Poupart. Robust One Round Federated Learning with Predictive Space Bayesian Inference
  14. Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin and Kai Chen. Secure Forward Aggregation for Vertical Federated Neural Networks
  15. Wenxin Wang, Jianyi Zhang, Zhi Sun, Zixiao Xiang and Yuyang Han. RRCM: A Fairness Framework for Federated Learning
  16. Ahmed Elkordy, Yahya H. Ezzeldin and Salman Avestimehr. Secure Federated Analytics for Set Intersection
  17. Martin Isaksson, Edvin Listo Zec, Rickard Cöster, Daniel Gillblad and Sarunas Girdzijauskas. Adaptive Expert Models for Personalization in Federated Learning
  18. Edvin Listo Zec, Ebba Ekblom, Martin Willbo, Olof Mogren and Sarunas Girdzijauskas. Decentralized adaptive clustering of deep nets is beneficial for client collaboration
  19. Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic and Gerald Quentin Maguire Jr. Fast Server Learning Rate Tuning for Coded Federated Dropout
  20. Wei Yu. TEE based Cross-silo Trustworthy Federated Learning Infrastructure
  21. Leye Wang, Chongru Huang and Xiao Han. Vertical Federated Knowledge Transfer via Representation Distillation
  22. Liu Yang, Junxue Zhang, Di Chai, Kai Chen and Qiang Yang. Practical and Secure Federated Recommendation with Personalized Mask
  23. Zijian Li, Jiawei Shao, Yuyi Mao, Jessie Hui Wang and Jun Zhang. Federated Learning with GAN-based Data Synthesis for Non-IID Clients
  24. Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan and Qiang Yang. An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
  25. Georgios Kollias, Theodoros Salonidis and Shiqiang Wang. Sketch to Skip and Select: Communication Efficient Federated Learning using Locality Sensitive Hashing

Call for Papers

Federated Learning (FL), a learning paradigm that enables collaborative training of machine learning models in which data reside and remain in distributed data silos during the training process. FL is a necessary framework to ensure AI thrive in the privacy-focused regulatory environment. As FL allows self-interested data owners to collaboratively train machine learning models, end-users can become co-creators of AI solutions. To enable open collaboration among FL co-creators and enhance the adoption of the federated learning paradigm, we envision that communities of data owners must self-organize during FL model training based on diverse notions of trustworthy federated learning, which include, but not limited to, security and robustness, privacy-preservation, interpretability, fairness, verifiability, transparency, auditability, incremental aggregation of shared learned models, and creating healthy market mechanisms to enable open dynamic collaboration among data owners under the FL paradigm. This workshop aims to bring together academic researchers and industry practitioners to address open issues in this interdisciplinary research area. For industry participants, we intend to create a forum to communicate problems are practically relevant. For academic participants, we hope to make it easier to become productive in this area. The workshop will focus on the theme of building trustworthiness into federated learning to enable open dynamic collaboration among data owners under the FL paradigm, and make FL solutions readily applicable to solve real-world problems.

Topics of interest include, but are not limited to:
Techniques:
  • Adversarial learning, data poisoning, adversarial examples,
    adversarial robustness, black box attacks
  • Architecture and privacy-preserving learning protocols
  • Auctions in federated learning
  • Auditable federated learning
  • Automated federated learning
  • Explainable federated learning
  • Fairness-aware federated learning
  • Federated learning and distributed privacy-preserving algorithms
  • Federated transfer learning
  • Human-in-the-loop for privacy-aware machine learning
  • Incentive mechanism and game theory for federated learning
  • Interpretable federated learning
  • Model merging and sharing
  • Personalization in federated learning
  • Privacy-aware knowledge driven federated learning
  • Privacy-preserving techniques (secure multi-party computation, homomorphic
    encryption, secret sharing techniques, differential privacy) for machine learning
  • Robustness in federated learning
  • Security for privacy, privacy leakage verification and self-healing etc.
  • Trade-off between privacy, safety, effectiveness and efficiency
  • Transparent federated learning
  • Verifiable federated learning
Applications:
  • Algorithm auditability
  • Approaches to make GDPR-compliant AI
  • Data value and economics of data federation
  • Open-source frameworks for privacy-preserving distributed learning
  • Safety and security assessment of federated learning
  • Solutions to data security and small-data challenges in industries
  • Standards of data privacy and security

More information on previous workshops can be found here.


Submission Instructions

Each submission can be up to 6 pages of contents plus up to 2 additional pages of references and acknowledgements. The submitted papers must be written in English and in PDF format according to the IJCAI'22 template. All submitted papers will be under a single-blinded peer review for their novelty, technical quality and impact. The submissions can contain author details. Submission will be accepted via the Easychair submission website.

Easychair submission site: https://easychair.org/conferences/?conf=fl-ijcai-22

For enquiries, please email to: fl-ijcai-22@easychair.org


Publications

   

For consideration of a post workshop LNAI publication, the organizing committee will invite a subset of accepted workshop papers to be extended and re-reviewed. More information regarding publications will be released at a later date.


Organizing Committee


Program Committee

  • Alysa Ziying Tan (Alibaba-NTU Singapore Joint Research Institute)
  • Andreas Holzinger (University of Natural Resources and Life Sciences)
  • Adriano Koshiyama (University College London/Holistic AI)
  • Anran Li (University of Science and Technology of China)
  • Bing Luo (City University of Hong Kong, Shenzhen)
  • Dimitrios Papadopoulos (Hong Kong University of Science and Technology)
  • Guojun Zhang (Huawei)
  • Grigory Malinovsky (King Abdullah University of Science and Technology)
  • Hongyi Peng (Alibaba-NTU Singapore Joint Research Institute)
  • Ji Feng (Sinovation Ventures)
  • Jiangtian Nie (Nanyang Technological University)
  • Jiankai Sun (The Ohio State University)
  • Jianshu Weng (Swiss Re)
  • Jianyu Wang (Carnegie Mellon University)
  • Jiawen Kang (Guangdong University of Technology)
  • Jihong Park (Deakin University)
  • Jinhyun So (University of Southern California)
  • Junxue Zhang (Clustar)
  • Kallista (Kaylee) Bonawitz (Google)
  • Kevin Hsieh (Microsoft Research)
  • Margaret Pan (China Telecom)
  • Mehrdad Mahdavi (Pennsylvania State University)
  • Mingyue Ji (University of Utah)
  • Paulo Ferreira (Dell)
  • Peng Zhang (Guangzhou University)
  • Philipp Slusallek (Saarland University)
  • Praneeth Vepakomma (Massachusetts Institute of Technology)
  • Rui Liu (Nanyang Technological University)
  • Rui-Xiao Zhang (Tsinghua University)
  • Shiqiang Wang (IBM)
  • Siwei Feng (Soochow University)
  • Songze Li (Hong Kong University of Science and Technology)
  • Stefan Wrobel (University of Bonn)
  • Theodoros Salonidis (IBM)
  • Victoria Wang (IEEE)
  • Wei Yang Bryan Lim (Alibaba-NTU Singapore Joint Research Institute)
  • Xiaohu Wu (Nanyang Technological University)
  • Xiaoli Tang (Nanyang Technological University)
  • Xi Chen (Huawei)
  • Xu Guo (Nanyang Technological University)
  • Yanci Zhang (Nanyang Technological University)
  • Yiqiang Chen (Chinese Academy of Sciences)
  • Yang Liu (Tsinghua University)
  • Yuan Liu (Northeastern University)
  • Yuang Jiang (Yale University)
  • Yuxin Shi (Alibaba-NTU Singapore Joint Research Institute)
  • Zelei Liu (Nanyang Technological University)
  • Zhuan Shi (University of Science and Technology of China)
  • Zichen Chen (University of California, Santa Barbara)

Sponsored by

     

FedML, Inc. (https://fedml.ai) aims to provide an end-to-end machine learning operating system for people or organizations to transform their data to intelligence with minimum efforts. FedML stands for “Fundamental Ecosystem Development/Design for Machine Learning” in a broad scope, and “Federated Machine Learning” in a specific scope. At the current stage, FedML is developing and maintaining a machine learning platform that enables zero-code, lightweight, cross-platform, and provably secure federated learning and analytics. It enables machine learning from decentralized data at various users/silos/edge nodes, without the need to centralize any data to the cloud, hence providing maximum privacy and efficiency. It consists of a lightweight and cross-platform Edge AI SDK that is deployable over edge GPUs, smartphones, and IoT devices. Furthermore, it also provides a user-friendly MLOps platform to simplify decentralized machine learning and real-world deployment. FedML supports vertical solutions across a broad range of industries (healthcare, finance, insurance, smart cities, IoT, etc.) and applications (computer vision, natural language processing, data mining, and time-series forecasting).

Organized by