Authors

* External authors

Venue

Date

Share

Byzantine-resilient Federated Learning via Gradient Memorization

Chen Chen

Lingjuan Lyu

Yuchen Liu*

Fangzhao Wu*

Chaochao Chen*

Gang Chen*

* External authors

FL-AAAI-22

2022

Abstract

Federated learning (FL) provides a privacy-aware learning framework by enabling a multitude of participants to jointly construct models without collecting their private training data. However, federated learning has exhibited vulnerabilities to Byzantine attacks. Many existing methods defend against such Byzantine attacks by monitoring the gradients of clients in the current round, i.e., gradients in one round. Recent works have demonstrated that such naïve methods can hardly achieve satisfying performance. Defenses based on one-round gradients could be compromised by adding a small well-crafted bias to the benign gradients, due to the high variance of one-round (benign) gradients. To address this problem, we propose a new Average of Gradients (AG) framework, which detects Byzantine attacks with the average of multi-round gradients (i.e., gradients across multiple rounds). We theoretically show that our AG framework leads to lower variance of the benign gradients, and thus can reduce the effects of Byzantine attacks. Experiments on various real-world datasets verify the efficacy of our AG framework.

Related Publications

MocoSFL: enabling cross-client collaborative self-supervised learning

ICLR, 2023
Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti*, Michael Spranger

Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…

IDEAL: Query-Efficient Data-Free Learning from Black-Box Models

ICLR, 2023
Jie Zhang, Chen Chen, Lingjuan Lyu

Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this prob…

Twofer: Tackling Continual Domain Shift with Simultaneous Domain Generalization and Adaptation

ICLR, 2023
Chenxi Liu*, Lixu Wang, Lingjuan Lyu, Chen Sun*, Xiao Wang*, Qi Zhu*

In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptat…

  • HOME
  • Publications
  • Byzantine-resilient Federated Learning via Gradient Memorization

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.