Data Valuation and Detections in Federated Learning

arXiv:2311.05304v3 Announce Type: replace
Abstract: Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a …

abstract arxiv challenge collaborative cs.ai cs.cr cs.lg data fair federated learning framework privacy quality quality data raw raw data training type valuation while