Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Despite that extensive works have studied the performance guarantee of the global model, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a novel notion, called {\em Fed-Influence}, to quantify this influence in terms of model parameter, and proposed an effective and efficient estimation algorithm. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenet of federated learning, without revealing any client's local data; and (3) it works well on both convex and non-convex loss functions and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset show that our estimation method can approximate Fed-Influence with small bias. Further, we demonstrated an application of client-level model debugging.

本文提出了一种新的概念——Fed-Influence，用于量化移动端设备对联合训练全局模型的影响力，并提出了一种有效的算法来估算这个指标，同时能够保证数据隐私不被泄露，并且适用于凸性和非凸性损失函数，在模型调试方面具有良好应用效果。

探索联邦学习中个体客户的影响