arXiv AI
LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning
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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 Machine Learning
Oct 12
PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks
PVBRF-ID is a two‑server federated learning protocol designed for oilfield enterprise networks that balances privacy and Byzantine robustness. It uses secret‑sharing of model updates, joint norm and cosine‑similarity checks against a clean root set, and cryptographic commitments to make accepted updates publicly verifiable. Experiments on a 15‑class Edge‑IIoT dataset show that PVBRF‑ID matches standard FedAvg on clean data, resists sign‑flip and adaptive poisoning attacks, and protects against update‑inference attacks while still allowing backdoor detection.
arXiv Machine Learning
Aug 4
FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices
arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
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.
arXiv Machine Learning
Sep 25
BRFID: Toward Byzantine-Robust Federated Intrusion Detection
The paper investigates the effects of label‑flipping poisoning attacks in a three‑client federated intrusion detection system (IDS) trained on CICIDS2017 with non‑IID attack subtype distributions. Flipping 60% of training labels from a single Byzantine client reduces the attacker’s own detection accuracy from 99.96% to 84.33%, while the federated global ensemble remains stable across all tested poison rates. The study shows that the self‑compromise signal can be detected as an anomaly, enabling Byzantine client identification without target data exfiltration, and notes that the current aggregation uses a Federated Forest rather than FedAvg, with future work planned to extend to parametric classifiers.
arXiv Machine Learning
Aug 24
BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning
arXiv:2608.21137v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...