线性编码联邦学习

Linear coding-based federated learning

联邦学习能够在边缘设备的协作训练中,保护边缘设备的数据隐私。而在通用联邦学习场景中,联邦学习的参与者通常由异构边缘设备构成,其中资源受限的设备会占用更长的时间,导致联邦学习系统的训练速度下降。现有方案或忽略掉队者,或根据分布式思想将计算任务进行分发,但是分发过程中涉及到原始数据的传递,无法保证数据隐私。为了缓解中小型规模的多异构设备协作训练场景下的掉队者问题,提出了编码联邦学习方案,结合线性编码的数学特性设计了高效调度算法,在确保数据隐私的同时,加速异构系统中联邦学习系统速度。同时,在实际实验平台中完成的实验结果表明,当异构设备之间性能差异较大时,编码联邦学习方案能将掉队者训练时间缩短92.85%。

Federated learning can protect the data privacy of edge devices in collaborative training of edge devices.In the general FL scenarios,the participants of FL are usually composed of heterogeneous edge devices,where resource-constrained devices will consume more time,resulting in the decline of the training speed.The existing schemes either ignore stragglers,or distribute the computing tasks according to the distributed idea,but the distribution process involves the transmission of raw data,which cannot guarantee data privacy.To alleviate the straggler problem in small or medium-sized multiple heterogeneous devices scenario,this paper proposed a coding-based FL scheme,and it designed an efficient scheduling algorithm combined with the mathematical characteristics of linear coding to ensure data privacy and accelerate the speed of heterogeneous FL system.Meanwhile,the experimental results completed in the actual experimental platform show that when the performance diffe-rence between heterogeneous devices is large,the coding-based FL scheme can shorten the training time of the straggler by 92.85%.