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With privacy legislation empowering the users with the right to be forgotten,it has become essential to make a model amenable for forgetting some of itstraining data. However, existing unlearning methods in the machine learningcontext can not be directly applied in the context of distributed settings likefederated learning due to the differences in learning protocol and the presenceof multiple actors. In this paper, we tackle the problem of federatedunlearning for the case of erasing a client by removing the influence of theirentire local data from the trained global model. To erase a client, we proposeto first perform local unlearning at the client to be erased, and then use thelocally unlearned model as the initialization to run very few rounds offederated learning between the server and the remaining clients to obtain theunlearned global model. We empirically evaluate our unlearning method byemploying multiple performance measures on three datasets, and demonstrate thatour unlearning method achieves comparable performance as the gold standardunlearning method of federated retraining from scratch, while beingsignificantly efficient. Unlike prior works, our unlearning method neitherrequires global access to the data used for training nor the history of theparameter updates to be stored by the server or any of the clients.

Federated Unlearning: How to Efficiently Erase a Client in FL?