CSRA: Robust Incentive Mechanism Design for Differentially Private Federated Learning

The differentially private federated learning (DPFL) paradigm emerges to firmly preserve data privacy from two perspectives. First, decentralized clients merely exchange model updates rather than raw data with a parameter server (PS) over multiple communication rounds for model training. Secondly, model updates to be exposed to the PS will be distorted by clients with differentially private (DP) noises. To incentivize clients to participate in DPFL, various incentive mechanisms have been proposed by existing works which reward participating clients based on their data quality and DP noise scales assuming that all clients are honest and genuinely report their DP noise scales. However, the PS cannot directly measure or observe DP noise scales leaving the vulnerability that clients can boost their rewards and lower DPFL utility by dishonestly reporting their DP noise scales. Through a quantitative study, we validate the adverse influence of dishonest clients in DPFL. To overcome this deficiency, we propose a robust incentive mechanism called client selection with reverse auction (CSRA) for DPFL. We prove that CSRA satisfies the properties of truthfulness, individual rationality, budget feasibility and computational efficiency. Besides, CSRA can prevent dishonest clients with two steps in each communication round. First, CSRA compares the variance of exposed model updates and claimed DP noise scale for each individual to identify suspicious clients. Second, suspicious clients will be further clustered based on their model updates to finally identify dishonest clients. Once dishonest clients are identified, CSRA will not only remove them from the current round but also lower their probability of being selected in subsequent rounds. Extensive experimental results demonstrate that CSRA can provide robust incentive against dishonest clients in DPFL and significantly outperform other baselines on three real public datasets.