Asymmetric Partial Model Transmission for Federated Edge Learning

Communication-Efficient Over-the-Air Federated Learning via Lightweight Gradient Compression

Integrating over-the-air computations into the model aggregation process of federated learning (FL) offers a promising solution to mitigate the communication bottleneck in FL model training. In this approach, all the clients modulate their intermediate parameters, such as gradients, onto the same set of orthogonal waveforms and transmit the resulting signals to the edge server simultaneously. Capitalizing on the superposition property of the radio channel, the server can extract an automatically aggregated global gradient from the received radio signal. However, the limited number of orthogonal waveforms imposes a constraint on the dimensionality of transmittable updates, hindering the adoption of more advanced, but high-dimensional models. In light of this challenge, we propose OFLight, a lightweight, yet effective, gradient compression algorithm tailored for OTA-FL systems. Specifically, in each communication round, the edge server constructs a low-rank projection matrix based on the received gradient matrix from the previous round (initialized with an independent and identically distributed standard normal matrix in the first round) and broadcasts it, along with the global model, to all clients in the system. Based on this matrix, every client projects its locally updated gradient matrix into a low-dimensional subspace through a linear operation. The clients upload only their compressed gradients via OTA computations, and the edge server can perform a linear decompression on the received signal, retrieving the original gradient dimension. Moreover, an error feedback mechanism is incorporated to compensate for the approximation error under aggressive compression. We derive analytical expressions for the convergence rate of both convex and non-convex loss functions, quantitatively demonstrating the effect of OFLight on the OTA-FL training efficiency. We also conduct extensive experiments to corroborate the efficacy of the proposed method.

GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks

A green quantized FSL (GQ-FSL) framework that incorporates stochastic quantization for both local collaborative training and wireless transmissions and enables large-scale DNN deployment on resource-constrained devices, achieving superior energy efficiency compared to quantized federated learning and full-precision FSL.

DASFL: Dynamic Adaptive Split Federated Learning for Heterogeneous Clients

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.

GCFed: Exploiting Gradient Correlation for Client Selection and Rate Allocation in Federated Learning

One-Shot Federated Learning With Lightweight Intermediate Models in Wireless Sensor Network Setting

One-shot federated learning (FL) completes model training and aggregation in a single communication round, significantly reducing communication costs compared to traditional FL. This approach is particularly suitable for resource-constrained environments such as wireless sensor networks (WSNs). However, existing solutions face significant challenges in aggregation owing to model heterogeneity, where clients adopt architectures of varying depth, width, and computational capacity. To address this issue, we propose a one-shot FL method named FedLIM, which employs a lightweight intermediate model for efficient knowledge transfer and global model aggregation. The Fisher information matrix (FIM) is incorporated to guide the model aggregation process and improve its robustness. Although FedLIM completes global training and aggregation in a single communication round, an optional personalized model adjustment step is introduced afterward. This step only involves server-to-client distribution without additional aggregation. Experimental results on three datasets demonstrate that FedLIM achieves superior global model accuracy compared to existing one-shot FL methods, particularly in highly heterogeneous environments. Moreover, the accuracy of local models is further enhanced through this optional refinement step.