Federated Learning

Heiko Ludwig is a Senior Manager, AI Platforms and a Principal Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Heiko coordinates the Federated Learning program at IBM Research and oversees the Distributed AI research area. His research contributed to different products, including IBM’s machine learning products. He is an ACM Distinguished Engineer and has more than 150 publications with more than 8000 citations. His technical work led to a number of technical awards by IBM and his numerous patents and patent applications received a designation as an IBM Master Inventor. Heiko is a co-editor in chief of the International Journal of Cooperative Information Systems and serves on the editorial boards of multiple journals. Heiko also serves regularly as program committee chair in conferences in the field. Heiko's wider interest is on large scale and cross-organizational AI systems and its related distributed systems, security and privacy research issues.Heiko received a doctorate in information systems from Otto-Friedrich-Universität Bamberg, Germany.Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Nathalie has led her team to the design of IBM Federated Learning framework which is now part of the Watson Machine Learning product and continues to work on its expansion. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation.  Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI Initiative. Nathalie has been invited to give multiple talks on federated learning, its challenges and opportunities. Nathalie has received four best paper awards and published in top-tier conferences and journals, obtaining more than 1300 Google scholar citations. Nathalie’s wider research interests include security and privacy, distributed systems and machine learning. Nathalie is also Associate Editor of the IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016.

• Introduction to Federated Learning.- Tree-Based Models for Federated Learning Systems.- Semantic Vectorization: Text and Graph-Based Models.- Personalization in Federated Learning.- Personalized, Robust Federated Learning with Fed+.- Communication-Efficient Distributed Optimization Algorithms.- Communication-Efficient Model Fusion.- Federated Learning and Fairness.- Introduction to Federated Learning Systems.- Local Training and Scalability of Federated Learning Systems.- Straggler Management.- Systems Bias in Federated Learning.- Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?.- Private Parameter Aggregation for Federated Learning.- Data Leakage in Federated Learning.- Security and Robustness in Federated Machine Learning.- Dealing with Byzantine Threats to Neural Networks.- Privacy-Preserving Vertical Federated Learning.- Split Learning: A Resource Efficient Model & Data Parallel Approach for Distributed Deep Learning.- Federated Learning for Collaborative Financial Crimes Detection.- Federated Reinforcement Learning for Portfolio Management.- Application of Federated Learning in Medical Imaging.- Advancing Healthcare Solutions with Federated Learning.- A Privacy-preserving Product Recommender System.- Application of Federated Learning in Telecommunications and Edge Computing.