Meta-Curriculum Federated Learning for Adaptive Task-Oriented Semantic Communication

Task-oriented semantic communication (TOSC) envisions efficient information exchange for the social Internet of Things (SIoT). However, deploying federated learning (FL) for TOSC faces critical challenges arising from non-independent and identically distributed (non-IID) data and diverse fading channels. Conventional static curriculum methods are often impractical in such dynamic and resource-limited environments. In this letter, we propose a novel framework that incorporates meta-curriculum learning (MCL) into the federated semantic communication to achieve adaptive and robust training. Specifically, we design a lightweight, plug-and-play reinforcement learning (RL) agent that dynamically optimizes local data scheduling via intrinsic feedback, eliminating dependencies on external public datasets. Simulation results demonstrate that MCL reduces communication rounds by 56% compared to static curriculum learning within the FedAvg and FedDF frameworks, confirming its scalability and generalizability in heterogeneous edge networks.