Trustworthy Federated Learning!
Updated: Apr 25
I will be giving a series of talks on "Trustworthy and Scalable Federated Learning" to highlight several exciting new results from our group.
Invited talk at FL-ICML'21
Invited seminar at Berkeley Laboratory for Information and System Sciences (BLISS)
Keynote at AI summit conference in Korea
Invited talk at CCF Advanced Disciplines Lecture on Privacy Preserving Machine Learning (Institute of Computing Technology of the Chinese Academy of Science)
Here is a video of one of the talks:
Here is also an abstract of my talks:
Trustworthy and Scalable Federated Learning
Federated learning (FL) is a promising framework for enabling privacy preserving machine learning across many decentralized users. Its key idea is to leverage local training at each user without the need for centralizing/moving any device's dataset in order to protect users’ privacy. In this talk, I will highlight several exciting research challenges for making such a decentralized system trustworthy and scalable to a large number of resource-constrained users. In particular, I will discuss three directions: (1) resilient and secure model aggregation, which is a key component and performance bottleneck in FL; (2) FL of large models, via knowledge transfer, over resource-constrained users; and (3) FedML, our open-source research library and benchmarking ecosystem for FL research (fedml.ai).
This talk is based on several papers: TurboAggregate (JSAIT’21, arXiv:2002.04156), Byzantine-Resilient Secure Federated Learning (JSAC’20, arXiv:2007.11115), FedGKT (NeurIPS’20, arXiv:2007.14513), FedNAS (CVPR-NAS’20, arXiv:2004.08546), FedML (NeurIPS-SpicyFL’20, arXiv:2007.13518), and FedGraphNN (ICLR - DPML 2021 & MLSys - GNNSys'21).