FLISC$^{3}$: Federated Learning-Oriented Resource Optimization in ISCC-Enabled Edge Collaborative Networks

Federated edge learning (FEEL) greatly facilitates the development of ubiquitous intelligence by combining federated learning and edge computing. However, traditional FEEL implementations assume fixed-sized local datasets, neglecting the potential of edge devices to acquire sensory information actively. Such a simplistic scenario leads to overestimating data availability and underestimating resource utilization in networks with varying resource capacity. Moreover, the existing FEEL-oriented systems with integrated sensing, communication, and computation (ISCC) have separate-based designs, leading to an inefficient use of wireless resources. To alleviate these issues, we propose a novel FEEL-oriented ISCC framework in edge collaborative networks, by leveraging the integrated sensing and communication (ISAC) technique to achieve the dual purpose of data sensing and parameter transmission. Then, over the designed framework, we present FEEL convergence analysis under non-independent and identically distributed (non-iid) and iid data. Correspondingly, we formulate a joint beamforming and flexible time duration optimization problem to maximize the convergence speed of FEEL, subject to limited resources on the devices and requirements for data sensing and communication. To address the problem efficiently, we propose an alternative optimization framework, in which the successive convex approximation (SCA) method is adopted to solve the nonconvex beamforming design subproblem, and a low-complexity method is derived for optimal time allocation. Extensive results reveal that the proposed framework can achieve excellent performance in model training accuracy by efficiently utilizing limited resources in edge collaborative networks, under iid and non-iid data.