Strategic Incentivization for Locally Differentially Private Federated Learning

• URL: http://arxiv.org/abs/2508.07138v1
• Date: Sun, 10 Aug 2025 01:57:47 GMT
• Title: Strategic Incentivization for Locally Differentially Private Federated Learning
• Authors: Yashwant Krishna Pagoti, Arunesh Sinha, Shamik Sural,
• Abstract summary: In Federated Learning (FL), multiple clients jointly train a machine learning model by sharing gradient information, instead of raw data, with a server over multiple rounds.<n>In Local Differential Privacy (LDP), clients add a selective amount of noise to the gradients before sending the same to the server.<n>In this paper, we model this privacy-accuracy trade-off as a game, where the sever incentivizes the clients to add a lower degree of noise for achieving higher accuracy.
• Score: 12.107007441973938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Federated Learning (FL), multiple clients jointly train a machine learning model by sharing gradient information, instead of raw data, with a server over multiple rounds. To address the possibility of information leakage in spite of sharing only the gradients, Local Differential Privacy (LDP) is often used. In LDP, clients add a selective amount of noise to the gradients before sending the same to the server. Although such noise addition protects the privacy of clients, it leads to a degradation in global model accuracy. In this paper, we model this privacy-accuracy trade-off as a game, where the sever incentivizes the clients to add a lower degree of noise for achieving higher accuracy, while the clients attempt to preserve their privacy at the cost of a potential loss in accuracy. A token based incentivization mechanism is introduced in which the quantum of tokens credited to a client in an FL round is a function of the degree of perturbation of its gradients. The client can later access a newly updated global model only after acquiring enough tokens, which are to be deducted from its balance. We identify the players, their actions and payoff, and perform a strategic analysis of the game. Extensive experiments were carried out to study the impact of different parameters.

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