← Back to all news
arXiv Machine Learning By Lihan Xu, Xiaoyi Fan, Gang Wang, Runhao Zeng, Xiping Hu, Yanjie Dong

A Nesterov-Accelerated Byzantine-Robust Federated Learning

Read the original on arXiv Machine Learning →

The paper proposes Byrd-NAFL, a Byzantine‑robust federated learning algorithm that incorporates Nesterov’s momentum and resilient aggregation rules. It achieves fast and safe convergence under non‑convex, smooth loss functions with relaxed gradient assumptions, and provides a finite‑time convergence guarantee. Experiments show that Byrd-NAFL outperforms existing methods in convergence speed, accuracy, and resilience to various malicious attacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 15

A Byzantine-Robust Federated Learning with Nesterov Momentum

The paper introduces Byrd-NAFL, a Byzantine‑robust federated learning algorithm that incorporates Nesterov momentum at the server while keeping workers stateless. It applies any soft γ‑Byzantine resilient aggregation rule and provides finite‑time convergence guarantees for smooth non‑convex losses across all momentum factors β∈[0,1). Experiments on MNIST, CIFAR‑10, and Sentiment140 demonstrate faster convergence and higher accuracy than existing benchmarks under various attacks.

By Lihan Xu, Xiaoyi Fan, Gang Wang, Runhao Zeng, Xiping Hu, Yanjie Dong
agentsbenchmarks
More like this →
arXiv Machine Learning
Oct 2

Robustifying Asynchronous SGD via Soft Throttling

arXiv:2609.39357v1 Announce Type: new Abstract: Asynchronous SGD is a popular algorithm for distributed learning where each client's gradient update is applied on arrival. This leads to a speed-up, b...

By Kaoru Otsuka, Maxime Meyer, Yuki Takezawa, Makoto Yamada, Anastasia Koloskova
More like this →