arXiv Machine Learning
FedSharp: Byzantine Robust Decentralized Federated Learning Under Noisy Channel
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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.
arXiv Machine Learning
Oct 15
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning
arXiv:2610.20264v1 Announce Type: new Abstract: Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minim...
arXiv AI
Sep 21
dSTAR: Straggler Tolerant and Byzantine Resilient Distributed SGD
arXiv:2412. 07151v1 Announce Type: cross Abstract: Distributed model training needs to be adapted to challenges such as the straggler effect and Byzantine attacks.
arXiv Machine Learning
Sep 4
A Nesterov-Accelerated Byzantine-Robust Federated 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.
arXiv AI
Sep 2
Optimizing Byzantine Node Placement in Decentralized Federated Learning
The paper investigates how the strategic placement of Byzantine nodes in decentralized federated learning (DFL) affects the propagation of malicious influence across the communication graph. It introduces Byzantine Placement Influence (BPI), a measure that captures cumulative exposure of honest nodes to Byzantine sources over time, and develops algorithms to optimize BPI across various network structures and attack types. Experiments demonstrate that BPI-guided placements consistently yield highly damaging configurations, highlighting the importance of considering node placement in DFL threat models.
arXiv Machine Learning
Aug 20
FedLNS: Leverage LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLMs
FedLNS is a server‑side framework that screens federated learning updates by representing each client’s contribution through changes in trainable normalization‑layer parameters, creating lightweight signatures that can be compared against a history‑aware cross‑client reference. The method requires no extra client‑to‑server communication, raw data, or labeled attack examples, and after screening, the remaining full‑model updates are aggregated with standard federated learning rules. Experiments on GPT‑style, BERT‑style, and LLaMA‑style models with 200 clients demonstrate that FedLNS achieves lower test perplexity than six baselines even when 40% of the population performs target manipulation under both IID and non‑IID data partitions.