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

Finding a needle in a haystack: A Black-Box Approach to Invisible Watermark Detection

ECCV, 2024
Minzhou Pan*, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin*

In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non watermarked dataset as a ref…

PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR

ICML, 2024
Kartik Patwari, Chen-Nee Chuah*, Lingjuan Lyu, Vivek Sharma

Current image anonymization techniques, largely focus on localized pseudonymization, typically modify identifiable features like faces or full bodies and evaluate anonymity through metrics such as detection and re-identification rates. However, this approach often overlooks …

COALA: A Practical and Vision-Centric Federated Learning Platform

ICML, 2024
Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of benchmarks for practical FL scenarios, which we categorize as task, data, and model levels. At the task level, COALA extends support from simple classification to 15 computer vision tasks, in…

  • 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.