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...
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:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
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.
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: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...
The paper investigates how privacy guarantees, robustness to Byzantine attacks, and detection coverage for rare intrusion types interact in federated network intrusion detection systems. It introduces geometric indistinguishability to explain how privacy noise can obscure minority-class signals, and demonstrates through experiments on UNSW‑NB15 that combining differential privacy with robust aggregation can disproportionately harm detection of rare attacks. The study highlights that these properties cannot be treated as independently composable and calls for aggregation‑aware modeling and sample‑aware evaluation to build trustworthy federated NIDS.
The paper investigates privacy and integrity risks in federated fine‑tuning of a pretrained MobileNetV4 model on Quick, Draw! data. It shows that a single client update can reveal private labels, that label‑count recovery degrades with more clients, and that backdoor and label‑flipping attacks can succeed under FedAvg but are mitigated by FLTrust. The study also demonstrates a live poisoning session using Indigma’s FedMaestro orchestrator.
arXiv:2608. 06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior.
The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task involving fraud and clinical‑risk scoring, where it improves the F1‑score for a minority class from 0.030 (plain FedAvg) to 0.119 while bounding privacy loss. The study demonstrates that privacy and robustness mechanisms interact, and that system design for regulated, adversarial, cross‑institutional settings must account for this interaction.
Giskard is a scalable protocol for private and Byzantine‑robust decentralized aggregation in large‑scale learning. It organizes participants into a tree of committees of size O(log n) and computes a differentially private coordinate‑wise approximate median using secure multi‑party computation within each committee. Experiments with up to one million participants show that Giskard reduces per‑party communication by nearly three orders of magnitude compared to its closest competitors while maintaining robustness against up to n/4 Byzantine parties.
The paper investigates how adversarial examples transfer between client models in federated learning and explores the relationship between these examples and client data distributions. It proposes a defense strategy based on adversarial training that leverages the transferability of model robustness. Experiments on real-life datasets demonstrate that the new attack and defense methods outperform existing state‑of‑the‑art approaches.