Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
This addresses the challenge of fair client selection and compensation in FL, offering a robust solution against Byzantine attacks, though it appears incremental as it builds on existing FL frameworks.
The paper tackles the problem of evaluating client contributions in Federated Learning under non-iid data and malicious attacks, introducing FRECA, which accurately quantifies contributions without needing validation data.
The performance of clients in Federated Learning (FL) can vary due to various reasons. Assessing the contributions of each client is crucial for client selection and compensation. It is challenging because clients often have non-independent and identically distributed (non-iid) data, leading to potentially noisy or divergent updates. The risk of malicious clients amplifies the challenge especially when there's no access to clients' local data or a benchmark root dataset. In this paper, we introduce a novel method called Fair, Robust, and Efficient Client Assessment (FRECA) for quantifying client contributions in FL. FRECA employs a framework called FedTruth to estimate the global model's ground truth update, balancing contributions from all clients while filtering out impacts from malicious ones. This approach is robust against Byzantine attacks and incorporates a Byzantine-resilient aggregation algorithm. FRECA is also efficient, as it operates solely on local model updates and requires no validation operations or datasets. Our experimental results show that FRECA can accurately and efficiently quantify client contributions in a robust manner.