Toward Understanding the Influence of Individual Clients in Federated
Learning
- URL: http://arxiv.org/abs/2012.10936v3
- Date: Tue, 13 Apr 2021 02:19:12 GMT
- Title: Toward Understanding the Influence of Individual Clients in Federated
Learning
- Authors: Yihao Xue, Chaoyue Niu, Zhenzhe Zheng, Shaojie Tang, Chengfei Lv, Fan
Wu, Guihai Chen
- Abstract summary: Federated learning allows clients to jointly train a global model without sending their private data to a central server.
We defined a new notion called em-Influence, quantify this influence over parameters, and proposed an effective efficient model to estimate this metric.
- Score: 52.07734799278535
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated learning allows mobile clients to jointly train a global model
without sending their private data to a central server. Extensive works have
studied the performance guarantee of the global model, however, it is still
unclear how each individual client influences the collaborative training
process. In this work, we defined a new notion, called {\em Fed-Influence}, to
quantify this influence over the model parameters, and proposed an effective
and efficient algorithm to estimate this metric. 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 tenets of federated learning, without revealing
any client's local private 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 demonstrate
that our estimation method can approximate Fed-Influence with small bias.
Further, we show an application of Fed-Influence in model debugging.
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