Decentralizing Compressed Sensing for Federated Learning with Hardware-Software Codesign

Compressed Sensing (CS), a sparse signal compression technique, has been adopted in federated learning (FL) reduce to network communication overhead. However, the computational cost incurred by CS reconstruction is commonly ignored, often surpassing the benefits derived from communication compression. This inefficiency arises from two main factors: (1) current CPU architectures are ill-suited for efficiently performing critical reconstruction operations, leading to low computational efficiency; and (2) reconstruction is executed centrally and sequentially at the server, imposing substantial computational burdens when facing multiple clients. To address these issues, we propose HiFedCS, a novel framework enabling accelerated and distributed CS reconstruction in FL. HiFedCS comprises two key components: (1) a hardware layer, optimize the critical operators in the reconstruction process at the circuit level; and (2) a software layer, offload the centralized reconstruction task from the server to clients for distributed execution. We implemented a HiFedCS prototype on a Xilinx Alveo U55C FPGA. Experimental results show that HiFedCS achieves up to 7.57× and 6.61× speedups over CPU and GPU, respectively, in gradient reconstruction tasks, and provides 2.52× and 1.92× end-to-end acceleration in real-world FL applications.