联邦学习团队终于有了一个能动态衡量客户端贡献的方法——FedTSV解决了固定权重带来的不公平和训练不稳定问题，做分布式模型训练的开发者可以直接参考实验效果。
联邦学习面临客户端贡献不均和动态变化的问题，传统固定权重聚合方法导致学习偏差和不稳定。本文提出轨迹Shapley值（TSV），一种基于验证集和时序一致性的贡献度量，能评估每个客户端对全局模型优化轨迹的影响。基于TSV，作者设计了FedTSV自适应聚合方法，将每轮评估转化为动态客户端权重，实时应对异构和对抗性参与。在基准数据集上的实验表明，FedTSV加速收敛、提升鲁棒性，并实现更公平的贡献评估，为公平感知联邦优化提供了理论基础。
Fairness-Aware Federated Learning with Trajectory Shapley Value
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.