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.

翻译：在联邦学习（FL）中，客户端的性能可能因多种原因而异。评估每个客户端的贡献对于客户端选择和补偿至关重要。由于客户端通常拥有非独立同分布（non-iid）数据，导致更新可能带有噪声或发散，因此这一任务具有挑战性。恶意客户端的存在进一步加剧了困难，尤其是在无法访问客户端本地数据或基准根数据集的情况下。本文提出了一种名为公平、鲁棒且高效的客户端评估（FRECA）的新方法，用于量化联邦学习中的客户端贡献。FRECA采用名为FedTruth的框架来估计全局模型的真实更新，平衡所有客户端的贡献，同时过滤掉恶意客户端的影响。该方法能够抵御拜占庭攻击，并集成了拜占庭鲁棒的聚合算法。FRECA还具有高效性，因为它仅基于本地模型更新运行，无需验证操作或数据集。实验结果表明，FRECA能够以鲁棒的方式准确且高效地量化客户端贡献。