In this work, we propose a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, taking into account limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on the MUs' provided information/features. To mitigate straggling problems with privacy-awareness, each selected MU can then encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, each selected MU can propose a contract to the MAP according to its expected trainable local data and privacy-protected encrypted data. To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging FL-based multiple utility functions. These utility functions account for the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Then, we transform the problem into an equivalent low-complexity problem and develop a light-weight iterative algorithm to effectively find the optimal solutions. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing the network's social welfare, i.e., total utility of all participating entities, up to 114% under the privacy cost consideration compared with those of baseline methods.
翻译:在这项工作中,我们提出一个新的框架,以解决联邦学习(FL)移动应用服务(MUs/移动应用程序提供商(MAP)的计算/通信资源有限,隐私成本,MUs之间在向MAP提供数据方面的合理性和激励竞争,特别是,MAP首先根据MU提供的信息/功能,为FL进程确定一套最佳的MU。为了减少隐私意识方面的棘手问题,每个选定的MU随后可以加密部分当地数据,并将加密数据上传到MAP的加密培训流程(MAP)、隐私成本成本成本成本成本成本成本成本成本成本成本成本成本成本成本、成本成本等值的多机构,同时通过IMU的数据向IMAP提出一个合同合同合同合同合同,同时向IMU提供最优化的MU和所有参与的 MMU问题。在使用FL-LMU数据的过程中,我们开发一个成本比重的多语言成本标准、成本等值的ILVLTeral-MU 运行一个成本总和时间等值的系统。