Joint Communication and Over-the-Air Computation for Semi-Federated Learning Towards Scalable AI in Computing-Heterogeneous IoT Systems

Abstract

The proliferation of Internet of Things (IoT) systems demands scalable artificial intelligence (AI) solutions that can operate in computing-heterogeneous environments with diverse hardware capabilities and non-independent and identically distributed data. This paper proposes a semi-federated learning (SemiFL) framework that integrates centralized learning (CL) and federated learning (FL) to enable efficient model training across heterogeneous IoT devices. In SemiFL, only devices with sufficient computational resources are designated for local model training (referred to as FL users), while the remaining devices transmit raw data to a base station (BS) for remote computation (referred to as CL users). This collaborative computing framework enables all IoT devices to participate in global model training, regardless of their computational capabilities and data distributions. Furthermore, to alleviate radio resource scarcity of SemiFL, we propose a joint communication and over-the-air computation design that unifies wireless transmission and model aggregation. This approach reduces latency and enhances spectrum efficiency by allowing simultaneous communication and computation over the air. Furthermore, we design a transceiver architecture that integrates receive beamforming and successive interference cancellation to mitigate multi-user interference, ensuring reliable aggregation at the BS. Subsequently, two scenarios are examined to assess the efficacy of SemiFL within IoT systems. Simulation results demonstrate that the proposed next-generation multiple access (NGMA)-based SemiFL outperforms the fixed beamforming-based FL by 8% and the AirComp-based FL by 6.4%, demonstrating its superior learning efficiency and convergence performance.