联邦学习中客户招募的动态定价
Dynamic Pricing for Client Recruitment in Federated Learning
摘要：
虽然联邦学习（FL）很好地保护了客户的数据隐私，但考虑到移动设备的通信成本和能耗，许多客户仍然不愿意加入FL。重要的是设计价格补偿，以激励足够的客户加入FL和分布式训练全球模型。现有的FL定价机制是静态的，不能适应客户随时间的随机到达模式。我们提出了一个新的动态定价解决方案，在封闭的形式，通过构建汉密尔顿函数，以最佳平衡客户招聘时间和模型的培训时间，而不知道客户的实际到达或培训成本。在客户招募阶段，我们为每个客户提供与时间相关的货币奖励，以在总付款和FL模型的准确性损失之间进行权衡。当我们接近招聘截止日期或数据老化更严重时，这种奖励会逐渐增加，如果客户每次迭代的培训时间变短，我们也会延长截止日期。此外，我们扩展到考虑异构客户端类型的训练数据大小和每次迭代的训练时间。我们成功地扩展了我们的动态定价解决方案，并开发了一个线性复杂度的最优算法来单调地选择FL的客户端类型。最后，我们还展示了我们的解决方案对客户端的数据大小的估计误差的鲁棒性，并运行数值实验来验证我们的结论。
英文摘要：
Though federated learning (FL) well preserves clients' data privacy, many clients are still reluctant to join FL given the communication cost and energy consumption in their mobile devices. It is important to design pricing compensations to motivate enough clients to join FL and distributively train the global model. Prior pricing mechanisms for FL are static and cannot adapt to clients' random arrival pattern over time. We propose a new dynamic pricing solution in closed-form by constructing the Hamiltonian function to optimally balance the client recruitment time and the model training time, without knowing clients' actual arrivals or training costs. During the client recruitment phase, we offer time-dependent monetary rewards per client arrival to trade-off between the total payment and the FL model's accuracy loss. Such reward gradually increases when we approach to the recruitment deadline or have greater data aging, and we also extend the deadline if the clients' training time per iteration becomes shorter. Further, we extend to consider heterogeneous client types in training data size and training time per iteration. We successfully extend our dynamic pricing solution and develop an optimal algorithm of linear complexity to monotonically select client types for FL. Finally, we also show the robustness of our solution against estimation error of clients' data sizes and run numerical experiments to validate our conclusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
