The paper introduces Layer-wise Budgeted Adaptive Transmission (LBAT), a method that treats federated learning communication as a resource allocation problem under tight uplink budgets. LBAT estimates the transmission value of each layer using local training signals and then applies a byte‑level dynamic programming allocator to choose optimal rank and bit configurations for each layer. Experiments on heterogeneous federated tabular prediction and data generation tasks show that LBAT outperforms uniform compression baselines, achieving better communication‑utility tradeoffs while maintaining distributional fidelity.
By Pengfei Li, Mohammad Khalil
arXiv:2508. 06692v2 Announce Type: replace Abstract: Federated learning systems typically allocate gradient compression by link speed.
By Md. Akmol Masud, Md Abrar Jahin, Mahmud Hasan
arXiv:2610.07466v1 Announce Type: new
Abstract: Multimodal systems often encode every available input, even when a subset suffices for prediction. Adaptive acquisition can reduce this cost by using p...
By Payal Mohapatra, Haodong Yang, Yueyuan Sui, Stephen Xia, Benjamin Lundell, Qi Zhu
LightSplit introduces a lightweight orthogonal random projection at the split learning cut layer to reduce communication overhead and limit information exposure. By sending low‑dimensional projections instead of raw activations, the method preserves end‑to‑end differentiability while discarding irreversibly part of the representation, thereby mitigating reconstruction attacks. Experiments on seven benchmarks show up to 32× compression with minimal accuracy loss and strong resistance to state‑of‑the‑art inversion attacks.
By Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger, Antonino Nocera, Ahmad-Reza Sadeghi
The paper introduces EMMI, a framework that enables communication‑efficient inference of multimodal large language models (MLLMs) on edge devices. EMMI encodes each sensor modality separately, fuses the representations, and compresses them into a compact latent vector that is transmitted to a server for high‑capacity reasoning. Experiments on a multimodal benchmark show that EMMI can cut the communication payload by 32× while keeping accuracy comparable, achieving up to a 3.4× reduction in end‑to‑end inference latency under bandwidth‑constrained conditions.
By Motahare Mounesan, Irfan Khan
arXiv:2607. 06651v1 Announce Type: new Abstract: Federated learning (FL) over mobile and edge devices increasingly involves multimodal models in which clients differ in both sensing capability and computational capacity.
By Quoc Bao Phan, Tuy Tan Nguyen