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

TL;DR

This paper introduces a semi-federated learning framework for IoT systems that combines centralized and federated learning, utilizing joint communication and over-the-air computation to improve efficiency and scalability in heterogeneous environments.

Contribution

It proposes a novel SemiFL framework integrating CL and FL, along with a joint communication and over-the-air computation design for IoT systems with heterogeneous devices.

Findings

SemiFL outperforms fixed beamforming FL by 8%.

SemiFL outperforms AirComp-based FL by 6.4%.

The proposed approach improves learning efficiency and convergence.

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.