federated-learning-57fdfde9·3 events·first seen Aliases: Federated Learning
This paper introduces the Trajectory Shapley Value (TSV), a contribution metric that evaluates each federated learning client's influence on the global model's optimization trajectory using validation-based, temporally consistent utility. Building on TSV, the authors propose FedTSV, an adaptive aggregation method that converts per-round evaluations into dynamic client weights to handle heterogeneous and adversarial participation. Experiments on benchmark datasets demonstrate improved convergence speed, robustness, and equitable contribution assessment compared to fixed-weight aggregation baselines.
This Hugging Face blog post describes how to combine the Hugging Face ecosystem with the Flower federated learning framework to train models across distributed, privacy-preserving data silos. It provides a practical walkthrough of integrating Transformers and Datasets libraries with Flower's federated training loop. The post targets practitioners looking to apply federated learning to NLP and other ML tasks without centralizing sensitive data.
This Hugging Face blog post covers Substra, a federated learning framework developed by Owkin for privacy-preserving AI. The post describes how Substra enables collaborative model training across institutions without sharing raw data, targeting healthcare and biomedical use cases. It represents a practical deployment pattern for federated learning in sensitive data environments.