Towards Fair, Robust and Efficient Client Contribution Evaluation in
  Federated Learning
Meiying Zhang Huan Zhao Sheldon Ebron Kan Yang
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.