Sageflow: Robust Federated Learning against Both Stragglers and AdversariesDownload PDF

21 May 2021, 20:48 (edited 21 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Federated Learning, Stragglers, Adversaries
  • TL;DR: We propose a new federated learning algorithm that handles both stragglers and adversaries simultaneously, via staleness-aware grouping with entropy-based filtering and loss-weighted averaging.
  • Abstract: While federated learning (FL) allows efficient model training with local data at edge devices, among major issues still to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries. While the presence of both of these issues raises serious concerns in practical FL systems, no known schemes or combinations of schemes effectively address them at the same time. We propose Sageflow, staleness-aware grouping with entropy-based filtering and loss-weighted averaging, to handle both stragglers and adversaries simultaneously. Model grouping and weighting according to staleness (arrival delay) provides robustness against stragglers, while entropy-based filtering and loss-weighted averaging, working in a highly complementary fashion at each grouping stage, counter a wide range of adversary attacks. A theoretical bound is established to provide key insights into the convergence behavior of Sageflow. Extensive experimental results show that Sageflow outperforms various existing methods aiming to handle stragglers/adversaries.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
23 Replies

Loading