Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple clients to train a model collaboratively by aggregating their local updates at a server. However, conventional aggregation schemes typically use fixed weights that fail to reflect unequal and time-varying client contributions, leading to biased and unstable learning. To improve fairness and stability, we propose the Trajectory Shapley Value (TSV), a contribution metric that evaluates how each client influences the optimization trajectory of the global model using a validation-based, temporally consistent utility. Building on TSV, we design FedTSV, an adaptive aggregation method that converts per-round evaluations into dynamic client weights, allowing the server to respond to heterogeneous and adversarial participation in real time. Experiments on benchmark datasets show that FedTSV accelerates convergence, improves robustness, and yields more equitable contribution assessments, thereby providing a principled foundation for fairness-aware federated optimization.

翻译：联邦学习是一种新兴的分布式范式，旨在应对异质性、隐私敏感数据带来的挑战。它允许多个客户端通过服务器聚合其本地更新，从而协同训练一个模型。然而，传统的聚合方案通常采用固定权重，无法反映客户端贡献的不平等及随时间变化的特性，导致学习过程存在偏倚且不稳定。为提升公平性与稳定性，我们提出了轨迹夏普利值（TSV），这是一种基于验证的、时间一致性效用度量，用于评估每个客户端对全局模型优化轨迹的影响。基于TSV，我们设计了FedTSV，一种自适应聚合方法，该方法将每轮评估转化为动态客户端权重，使服务器能够实时响应异质性和对抗性参与。在基准数据集上的实验表明，FedTSV能够加速收敛、提升鲁棒性，并产生更公平的贡献评估，从而为公平感知的联邦优化提供了原则性基础。