arXiv AI
Jul 23

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

arXiv:2607. 19532v1 Announce Type: cross Abstract: Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care.

By Ruth Amey, Muhammad Arifur Rahman, Taha Osman, Nicholas Shopland, Andy Burton, Mufti Mahmud, David J. Brown
Hugging Face Trending Papers
Jul 9

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity.

arXiv AI
5d ago

Efficiently Distributed Federated Learning

The paper introduces FastFederatedLearning (FFL), an open‑source federated learning framework written in C/C++ that prioritizes performance and customizability. FFL allows users to define arbitrary communication graphs between clients and servers, and it consistently outperforms Intel OpenFL with speedups of 2.5x to 3.69x across x86‑64, ARM‑v8, and RISC‑V platforms. Future plans include a Python wrapper and middleware for diverse communication backends, aiming to support dynamic, evolving federations as long‑term services.

By Gianluca Mittone, Robert Birke, Marco Aldinucci
arXiv Machine Learning
Aug 27

Differentiated Aggregation to Improve Generalization in Federated Learning

The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.

By Peyman Gholami, Hulya Seferoglu