DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
<|reference_start|>DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service: Federated learning (FL) has emerged as a prevalent distributed machine learning scheme that enables collaborative model training without aggregating raw data. Cloud service providers further embrace Federated Learning as a Service (FLaaS), allowing data analysts to execute their FL training pipelines over differentially-protected data. Due to the intrinsic properties of differential privacy, the enforced privacy level on data blocks can be viewed as a privacy budget that requires careful scheduling to cater to diverse training pipelines. Existing privacy budget scheduling studies prioritize either efficiency or fairness individually. In this paper, we propose DPBalance, a novel privacy budget scheduling mechanism that jointly optimizes both efficiency and fairness. We first develop a comprehensive utility function incorporating data analyst-level dominant shares and FL-specific performance metrics. A sequential allocation mechanism is then designed using the Lagrange multiplier method and effective greedy heuristics. We theoretically prove that DPBalance satisfies Pareto Efficiency, Sharing Incentive, Envy-Freeness, and Weak Strategy Proofness. We also theoretically prove the existence of a fairness-efficiency tradeoff in privacy budgeting. Extensive experiments demonstrate that DPBalance outperforms state-of-the-art solutions, achieving an average efficiency improvement of $1.44\times \sim 3.49 \times$, and an average fairness improvement of $1.37\times \sim 24.32 \times$.<|reference_end|>
@article{liu2024dpbalance:,
    title={DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated
  Learning as a Service},
    author={Yu Liu, Zibo Wang, Yifei Zhu, Chen Chen},
    journal={arXiv preprint arXiv:2402.09715},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09715},
    primaryClass={cs.DC cs.CR cs.LG}
}