Coded Federated Learning Framework for AI-Based Mobile Application Services with Privacy-Awareness

By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL), but also preserve privacy of sensitive data uploaded/contributed by participating mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP). However, these advantages come with extra coding cost/complexity and communication overhead (referred to as \emph{privacy cost}) that must be considered given the limited computing/communications resources at MUs/MAP, the rationality and incentive competition among MUs in contributing data to the MAP. This article proposes a novel coded FL-based framework for a privacy-aware mobile application service to address these challenges. In particular, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can propose a contract to the MAP according to its expected trainable local data and privacy-protected coded 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 coded FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resource, and asymmetric information between the MAP and MUs. Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to solve it. 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 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.