CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning

We present two novel coded federated learning (FL) schemes for linear regression that mitigate the effect of straggling devices. The first scheme, CodedPaddedFL, mitigates the effect of straggling devices while retaining the privacy level of conventional FL. Particularly, it combines one-time padding for user data privacy with gradient codes to yield resiliency against straggling devices. To apply one-time padding to real data, our scheme exploits a fixed-point arithmetic representation of the data. For a scenario with 25 devices, CodedPaddedFL achieves a speed-up factor of 6.6 and 9.2 for an accuracy of 95\% and 85\% on the MMIST and Fashion-MNIST datasets, respectively, compared to conventional FL. Furthermore, it yields similar performance in terms of latency compared to a recently proposed scheme by Prakash \emph{et al.} without the shortcoming of additional leakage of private data. The second scheme, CodedSecAgg, provides straggler resiliency and robustness against model inversion attacks and is based on Shamir's secret sharing. CodedSecAgg outperforms state-of-the-art secure aggregation schemes such as LightSecAgg by a speed-up factor of 6.6--14.6, depending on the number of colluding devices, on the MNIST dataset for a scenario with 120 devices, at the expense of a 30\% increase in latency compared to CodedPaddedFL.

Coding for Straggler Mitigation in Federated Learning

We present a novel coded federated learning (FL) scheme for linear regression that mitigates the effect of straggling devices while retaining the privacy level of conventional FL. The proposed scheme combines one-time padding to preserve privacy and gradient codes to yield resiliency against stragglers and consists of two phases. In the first phase, the devices share a one-time padded version of their local data with a subset of other devices. In the second phase, the devices and the central server collaboratively and iteratively train a global linear model using gradient codes on the one-time padded local data. To apply one-time padding to real data, our scheme exploits a fixed-point arithmetic representation of the data. Unlike the coded FL scheme recently introduced by Prakash et al., the proposed scheme maintains the same level of privacy as conventional FL while achieving a similar training time. Compared to conventional FL, we show that the proposed scheme achieves a training speed-up factor of $6.6$ and $9.2$ on the MNIST and Fashion-MNIST datasets for an accuracy of $95\%$ and $85\%$, respectively.