FedShapleX: Shapley Value Driven Context-Aware Model-Heterogeneous Federated Learning

Abstract

Model Heterogeneous Federated Learning(MHFL) builds on traditional Federated Learning (FL) to better leverage the knowledge and data distributed across hardware-heterogeneous devices. Among various heterogeneous FL approaches, the Partially Training (PT)-based methods are one of the most promising approaches, which extract submodels from the global model for local training. However, existing state-of-the-art(SOTA) methods lack effective guidance for updating the global model, making it challenging to handle the Non-IID data distribution and maintain generalization across clients. To guide the update of the global model to mitigate the impact of Non-IID data and enhance the generalization of the global model, we proposed FedShapleX: Shapley Value Driven Context-Aware Submodel Extraction for Model-Heterogeneous Federated Learning. In this work, we first proposed a Parameter-based Class-Specific Shapley Value (PCSV), which quantifies each client's class-specific contribution to the global model, providing a measure of how effectively the local knowledge is utilized. Leveraging the contribution assessment, we further develop a Reinforcement Learning-aided Large Neighbourhood Search Algorithm (RL-LNS) algorithm, which optimizes the submodel extraction scheme based on context-aware contribution information, thereby guiding the global model update more effectively. Leveraging the actor-critic scheme, the RL-LNS combines the strengths of Large Neighbourhood Search (LNS) and Reinforcement Learning (RL), improving the LNS's search efficiency while simplifying the design of RL policies. To validate the RL-LNS, we have compared the FedShaplex against the state-of-the-art (SOTA) partial training-based approach MHFL, the global model performance, and its average accuracy on clients' datasets.