arXiv Machine Learning
The Dynamics of Collapse and Recovery in Clustered Federated Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Oct 20
The Dynamics of Collapse and Recovery in Clustered Federated Learning
Clustered federated learning (CFL) can suffer from cluster collapse. In particular, under unfavorable initialization, early client assignments concentrate heterogeneous groups on one cluster model, wh...
arXiv Machine Learning
Oct 5
Scalar Federated Learning for Linear Quadratic Regulator
arXiv:2604.05088v2 Announce Type: replace-cross Abstract: We propose ScalarFedLQR, a communication-efficient federated algorithm for model-free learning of a common policy in linear quadratic regulat...
arXiv Machine Learning
Sep 7
Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning
The paper introduces FedSWE, a federated learning algorithm designed to handle non‑stationary and heterogeneous client availability without requiring prior real‑time knowledge of which devices are online. FedSWE compensates for missed computations, stabilizes global updates, and mixes local updates through implicit gossiping, all while adding only modest memory and computational overhead. The authors prove that FedSWE converges to a stationary point for non‑convex objectives and achieves linear speedup in certain scenarios, and they validate these claims with experiments on real‑world datasets featuring diverse client unavailability patterns.
arXiv Machine Learning
Jun 3
Lethe: Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
arXiv:2601. 22601v2 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model.
arXiv Machine Learning
Jun 30
Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning
arXiv:2606. 30499v1 Announce Type: new Abstract: Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversity of client distributions.
arXiv Machine Learning
Oct 9
When to Intervene? State-Aware Sparse Manipulation in Federated Reinforcement Learning
The paper introduces the Viability‑constrained Behavioral Steering Attack (V‑BSA), a new attack strategy for federated reinforcement learning that targets the timing of interventions rather than just the construction of malicious updates. By selecting sparse intervention states based on local policy uncertainty and applying envelope‑constrained steering, V‑BSA can significantly degrade performance on discrete‑action benchmarks while using far fewer interventions than traditional dense poisoning attacks. The study demonstrates that the specific states at which interventions occur constitute a distinct and critical dimension of robustness in sequential decision‑making settings.