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  • Salman Avestimehr

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.

Here is a video of one of the talks:



Here is also an abstract of my talks:

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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).


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