Data Valuation and Detections in Federated Learning
This addresses the challenge of data valuation in federated learning for researchers and practitioners, though it appears incremental as it builds on existing FL and Wasserstein distance concepts.
The paper tackles the problem of fair and efficient data valuation in federated learning to incentivize high-quality contributions, introducing FedBary, a privacy-preserving method that uses Wasserstein distance for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm.
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 negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein distance within the federated context, offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses, we demonstrate the potential of this data valuation method as a promising avenue for FL research.