3 research outputs found
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
Federated learning (FL) has achieved great success as a privacy-preserving
distributed training paradigm, where many edge devices collaboratively train a
machine learning model by sharing the model updates instead of the raw data
with a server. However, the heterogeneous computational and communication
resources of edge devices give rise to stragglers that significantly decelerate
the training process. To mitigate this issue, we propose a novel FL framework
named stochastic coded federated learning (SCFL) that leverages coded computing
techniques. In SCFL, before the training process starts, each edge device
uploads a privacy-preserving coded dataset to the server, which is generated by
adding Gaussian noise to the projected local dataset. During training, the
server computes gradients on the global coded dataset to compensate for the
missing model updates of the straggling devices. We design a gradient
aggregation scheme to ensure that the aggregated model update is an unbiased
estimate of the desired global update. Moreover, this aggregation scheme
enables periodical model averaging to improve the training efficiency. We
characterize the tradeoff between the convergence performance and privacy
guarantee of SCFL. In particular, a more noisy coded dataset provides stronger
privacy protection for edge devices but results in learning performance
degradation. We further develop a contract-based incentive mechanism to
coordinate such a conflict. The simulation results show that SCFL learns a
better model within the given time and achieves a better privacy-performance
tradeoff than the baseline methods. In addition, the proposed incentive
mechanism grants better training performance than the conventional Stackelberg
game approach