We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices' data across the network. Compared to other schemes in the literature, which deal with stragglers or device dropouts by ignoring their contribution, the proposed schemes do not suffer from the client drift problem. The first scheme, CodedPaddedFL, mitigates the effect of stragglers while retaining the privacy level of conventional FL. It combines one-time padding for user data privacy with gradient codes to yield straggler resiliency. The second scheme, CodedSecAgg, provides straggler resiliency and robustness against model inversion attacks and is based on Shamir's secret sharing. We apply CodedPaddedFL and CodedSecAgg to a classification problem. For a scenario with 120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of 95% on the MNIST dataset compared to conventional FL. Furthermore, it yields similar performance in terms of latency compared to a recently proposed scheme by Prakash et al. without the shortcoming of additional leakage of private data. CodedSecAgg outperforms the state-of-the-art secure aggregation scheme LightSecAgg by a speed-up factor of 6.6-18.7 for the MNIST dataset for an accuracy of 95%.
翻译:我们提出了两个新颖的联邦学习(FL)计划,通过在整个网络中引入设备数据冗余来减轻套用装置断层装置的影响。与文献中的其他计划相比,这些计划通过忽略用户贡献处理累进器或装置辍学问题,拟议方案没有受到客户漂流问题的影响。第一个方案,即编码PaddPadadadfil,在保留传统FL的隐私水平的同时,减轻套接器的影响。它结合了用户数据隐私的一次性套接格和梯度代码,以产生递解弹性。第二个方案,即编码SecAgg,针对模式的倒流攻击提供弹性和稳健性,并以Shamir的秘密共享为基础。我们对分类问题采用了编码PaddPad和编码SecadAgg。对于120个装置的假设,编码PaddPaddfl为MIT数据集的精确度为95%,而常规FL。此外,它与最近提议的SICA-SER的精确度计划相比,其精确性表现与最近提议的SEB-SB的精确度相似。