arXiv:2508. 06692v2 Announce Type: replace Abstract: Federated learning systems typically allocate gradient compression by link speed.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2609.39646v1 Announce Type: new Abstract: Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these upd...
The paper introduces ModalShare, a bandwidth allocation method for multimodal split learning that assigns each modality a keep‑ratio based on its Shapley contribution score. Unlike existing compression schemes that split the uplink budget proportionally to activation size, ModalShare explicitly optimizes the split across modalities, requiring no extra uplink traffic or client computation. Experiments on CREMA‑D and MVSA datasets show that ModalShare improves accuracy by 12.4–15.4 percentage points over equal keep‑ratios under a 5× compression budget, outperforming three compressors across multiple datasets and budgets.
FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.
arXiv:2607. 18343v1 Announce Type: cross Abstract: Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor.
The paper investigates how the time cost of 802.11 contention affects federated learning. Using ns-3 simulations, the authors measure frame-delivery ratios and saturation throughput across various client densities and offered loads, then employ a FedAvg trainer that uses these ratios to estimate communication time. Across 720 runs with diverse datasets, partitions, densities, loads, and seeds, all models reached target accuracy within the round budget, with communication time-to-target increasing significantly as client density rose. The study also compares uniform and persistent heterogeneous participation, finding no statistically significant accuracy gap, though confidence intervals are wide. The results are specific to the evaluated configurations and do not generalize to all convergence or fairness scenarios.
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.