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.

Mobile edge computing (MEC) pushes cloud computing capabilities to the network edge, which provides real-time processing and caching flexibility for service-based applications. Conventionally, the individual node solution is insufficient to tackle the increasing computation workload and provide diverse services, especially for unpredictable spatiotemporal service request patterns. To address this, we first propose a hierarchical collaborative computing (HCC) framework to serve users’ demands by reaping sufficient computing capability in Cloud, ubiquitous service area in edge layer, and idle resources in device layer. To better unleash the benefits of HCC and pursue long-term performance, we investigate heterogeneity-aware resource management by collaborative service placement, task scheduling, and resource allocation both in-node and cross-node. We then propose an online optimization framework that first decouples the decisions across different slots. For each instant mixed integer non-linear programming problem, we introduce the surrogate Lagrangian relaxation method to reduce complexity and design hybrid numerical techniques to solve the subproblems. Theoretical analysis and extensive simulation results demonstrate the efficiency of the HCC framework in decreasing system cost on devices, and our proposed algorithms can effectively utilize the resources in the collaborative space to achieve the trade-off between system cost minimization and service placement cost stability.

In this paper, the joint task offloading and resource allocation are investigated for the semi-grant-free (SGF) non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. Moreover, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are deployed to improve the quality of wireless communications under the mode switching protocol. Each MU can partially or fully offload its task to the base station (BS) based on its differentiated channel conditions and computing capacity in the proposed MEC system. We formulate the joint task offloading, channel assignment, power allocation, and the RIS coefficients design problem to save energy consumption. The formulated problem is modeled from a long-term optimization perspective as a multi-agent Markov game (MG). Then, a multi-agent deep reinforcement learning (MADRL) based joint task offloading and resource allocation (JTORA) algorithm is proposed to solve the problem. The simulation results confirm that the applied SGF-NOMA scheme can significantly reduce energy consumption under a stringent latency constraint. Moreover, the effectiveness of the STAR-RIS and the proposed algorithm are confirmed.