Federated Learning over 5G/6G Networks: Dynamic Client Selection and Resource Allocation for Heterogeneous Edge Environments

KaaS-Edge: Resource-Aware Knowledge Distillation Service for Heterogeneous Wireless Edge Networks

Federated distillation (FD) enables collaborative edge learning by exchanging soft predictions rather than model parameters, offering communication efficiency and architectural flexibility. However, deploying FD over heterogeneous wireless networks requires principled methods to schedule device participation and allocate upload volumes under per-round resource constraints. Existing approaches assume uniform participation or rely on heuristic selection, ignoring the coupling among communication cost, computational capability, and privacy posture across devices. This paper proposes KaaS-Edge, a Knowledge-as-a-Service framework that formulates device scheduling as budgeted submodular maximization. We derive an optimal water-filling volume allocation in closed form and present RADS (Resource-Aware Distillation Scheduling), a greedy algorithm with a constant-factor approximation guarantee. Experiments on CIFAR-100 demonstrate that KaaS-Edge achieves accuracy comparable to full-participation baselines while reducing per-round communication by nearly ten times and cumulative bandwidth by over an order of magnitude, with graceful degradation under stringent privacy constraints.

When Split Federated Learning Meets Prototype Learning: A Communication-Efficient Approach in Wireless Networks

Nowadays, split federated learning (SFL) has emerged as an effective paradigm for enabling privacy-preserving collaborative intelligence across heterogeneous devices with limited computation. However, SFL incurs significant communication overhead in wireless networks due to the uplink transmission of high-dimensional smashed data, which degrades network efficiency. To mitigate the communication bottleneck, we propose a prototype-based SFL framework ProtoSFL. Specifically, each selected client computes local prototypes for observed classes and uploads them to the server. Based on the received prototypes, the server derives global prototypes and optimizes a weighted objective that combines classification loss with prototype alignment loss. The server then updates the model accordingly and returns personalized prototype gradients to the clients. Simulation results verify the effectiveness of ProtoSFL in reducing communication overhead, achieving a substantial reduction in uplink communication, while maintaining competitive testing accuracy under various heterogeneous data settings compared with SFL baselines.

DASFL: Dynamic Adaptive Split Federated Learning for Heterogeneous Clients

Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability. In practical wireless networks, mobile devices (MDs) often exhibit diverse data and learning objectives, naturally forming clusters of users with jointly trainable models. When devices rely on energy harvesting (EH), stochastic energy arrivals further complicate participation and scheduling under communication constraints. In this work, we study over-the-air (OTA) FL with EH MDs under heterogeneous data distributions, and investigate two closely related learning objectives within a unified framework: one aiming for a more representative global model by reducing data bias, and the other learning more personalized cluster-specific models by exploiting this bias. In the global training mode, cluster information guides energy- and diversity-aware scheduling, ensuring that the scheduled active users provide a more representative aggregate update. In the personalization mode, the same cluster structure defines cluster-level learning objectives and OTA recovery targets, enabling the parameter server to train multiple cluster-specific models through simultaneous transmissions over the wireless multiple-access channel. Numerical results demonstrate that the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.

Bandwidth-Aware Decentralized Federated Learning in Wired Networks

RAD-SFL: Resource-Aware Dynamic Split Federated Learning for Heterogeneous Edge Environments with Non-IID Data

Edge computing enables distributed intelligence in resource-constrained IoT environments. However, traditional Federated Learning (FL) struggles with heterogeneous device capabilities, dynamic network conditions, and non-IID data distributions, resulting in straggler effects, slow convergence, and inefficient resource utilization. This paper proposes Resource-Aware Dynamic Split Federated Learning (RAD-SFL), a framework for efficient distributed training in heterogeneous edge environments. RAD-SFL introduces a dynamic model layer splitting mechanism that adaptively partitions model execution between client devices and edge servers based on real-time computation and communication conditions, and a group-and-reorder technique that organizes devices into balanced groups with similar data distributions to improve model convergence under non-IID settings. We validate RAD-SFL through experiments on widely adopted datasets using both a simulated environment and a real testbed with heterogeneous IoT devices. Results demonstrate that RAD-SFL reduces the training time by up to 66.4%, decreases device-side energy consumption by 52.5%, and improves global model accuracy by up to 30.5% compared to FL and SFL baselines.