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DPFL is a federated optimization algorithm for client-side DP preservation. Its aim is to hide the contribution of clients during training and balance the trade-off between privacy loss and model performance. Empirical studies show that when the number of participating clients is sufficiently large, the protocol proposed in the paper also requires only a small cost in terms of model performance to maintain DP at the client level. In the FL Framework, the server averages the client model (i.e., the weight matrix) after each round of communication. In DPFL, on the other hand, a random mechanism will be used to change and approximate this averaging. This is done in order to hide the contribution of individual clients in the set, and thus in the overall distributed federation learning process. |
IEEE/ICACT20220347 Slide.23
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