FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization

Federated Learning (FL) enables distributed training on edge devices but faces significant challenges due to resource constraints in edge environments, impacting both communication and computational efficiency. Existing iterative pruning techniques improve communication efficiency but are limited by their centralized design, which struggles with FL's decentralized and data-imbalanced nature, resulting in suboptimal sparsity levels. To address these issues, we propose FedPaI, a novel efficient FL framework that leverages Pruning at Initialization (PaI) to achieve extreme sparsity. FedPaI identifies optimal sparse connections at an early stage, maximizing model capacity and significantly reducing communication and computation overhead by fixing sparsity patterns at the start of training. To adapt to diverse hardware and software environments, FedPaI supports both structured and unstructured pruning. Additionally, we introduce personalized client-side pruning mechanisms for improved learning capacity and sparsity-aware server-side aggregation for enhanced efficiency. Experimental results demonstrate that FedPaI consistently outperforms existing efficient FL that applies conventional iterative pruning with significant leading in efficiency and model accuracy. For the first time, our proposed FedPaI achieves an extreme sparsity level of up to 98% without compromising the model accuracy compared to unpruned baselines, even under challenging non-IID settings. By employing our FedPaI with joint optimization of model learning capacity and sparsity, FL applications can benefit from faster convergence and accelerate the training by 6.4 to 7.9 times.

RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar Image Formation

Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes are received and later used by image formation algorithms to form a SAR image. There is great interest in using SAR images in computer vision tasks such as automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we show that deep learning can be used to train a neural network able to form SAR images from echo data. Results show that our neural network, RDAnet, can form SAR images comparable to images formed using a traditional algorithm. This approach opens the possibility to end-to-end SAR applications where image formation and image processing are integrated into a single task. We believe that this work is the first demonstration of deep learning based SAR image formation using real data.