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.
By Vasileios Perifanis, Nikolaos Pavlidis
arXiv:2609.07192v1 Announce Type: cross
Abstract: Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather...
By Prashant Bajpai, Divya Saxena, Philippe Lalanda, German Vega
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.
By Kai Li, Jong-Ik Park, Carlee Joe-Wong, Wei Ni, Falko Dressler
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...
By Tiantong Sun, Jinlin Liu, Xingru Wei, Zequan Fang, Wei Chen, Qiang Yang, Xiaojin Zhang
Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. Wh...
The paper introduces SWB-DM, a Byzantine‑robust federated learning aggregator that treats each slice of a client update as a one‑dimensional distribution, computes a trimmed Wasserstein barycenter across clients, and uses a medoid‑based gauge‑fixing step to recover coordinate identity. It further incorporates delayed‑momentum caching to decouple robustness from the specific clients sampled each round. Extensive experiments on CIFAR‑10, CIFAR‑100, FEMNIST, and a 500‑client scalability run reveal distinct failure modes of existing defenses and demonstrate that SWB‑DM achieves significant gains, especially when compared under equal round budgets.
By Saranraj S, Saranya M S, Alex David S, Ajay Kumar A
arXiv:2611.05546v1 Announce Type: new
Abstract: Decentralized federated learning (DFL) eliminates the central aggregation server but remains vulnerable to data heterogeneity, Byzantine attacks, and c...
By Ojaswi Sinha, Amin Farajzadeh, Animesh Yadav, Melike Erol-Kantarci
ORDERS is a personalized federated learning configuration that combines a shared backbone, private residual adapters, and a classifier, using geometric weights based on descending update norms, feature alignment, and private‑parameter perturbations. In extensive experiments on CIFAR‑10 and Sent140, ORDERS achieves slightly higher mean client accuracy than baseline methods, with a 5.47% reduction in parameter payload. Ablation studies show limited, dataset‑dependent benefits from norm ranking and alignment, and no clear advantage from perturbations.
By Koffka Khan
The paper proposes typed federated artifacts—schema‑validated objects with per‑field privacy and dispute resolution—to enable tool‑routing knowledge sharing among frozen, heterogeneous LLM agents. By replacing flat text prompts with typed fields, the authors achieve near‑centralized routing performance on StableToolBench while reducing data size to 20 MB JSON per client. The study also highlights that a simple TF‑IDF classifier can outperform LLM routing on labeled benchmarks, indicating limitations in current evaluation methods.
By Abhijit Chakraborty, Ni Trieu, Vivek Gupta
FedReview is a review-based mechanism for federated learning that identifies and removes poisoned model updates without needing a server-side validation dataset or historic client knowledge. In each training round, the server randomly selects reviewers who evaluate incoming updates on their own training data, rank them, and estimate how many low-quality updates are likely poisoned. The server then aggregates these rankings via majority voting to filter out suspicious updates during model aggregation, enabling robust global model training even in adversarial settings.
By Tianhang Zheng, Yanlu Li, Bohan Deng, Baochun Li
arXiv:2609.23463v1 Announce Type: new
Abstract: Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up po...
By Kiran Naseer, Samreen Azhar, Umar Shoaib, Haroon Mahmood, Muhammad Awais
arXiv:2610.27357v1 Announce Type: cross
Abstract: Byzantine-robust aggregation shields federated learning from malicious updates, but the rules that filter out suspect updates do so by comparing clie...
By Saheed A. Tijani, Frank Jiang, Ming Liu