Coded Computing for Federated Learning at the Edge
Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server. Performance of FL in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. A recent work, Coded Federated Learning (CFL), proposes to mitigate stragglers and speed up training for linear regression tasks by assigning redundant computations at the MEC server. Coding redundancy in CFL is computed by exploiting statistical properties of compute and communication delays. We develop CodedFedL that addresses the difficult task of extending CFL to distributed non-linear regression and classification problems with multioutput labels. The key innovation of our work is to exploit distributed kernel embedding using random Fourier features that transforms the training task into distributed linear regression. We provide an analytical solution for load allocation, and demonstrate significant performance gains for CodedFedL through experiments over benchmark datasets using practical network parameters.
Code (0)
등록된 구현이 없습니다.
Tasks
Edge-computingFederated LearningregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge comp…
Edge-computingFederated LearningCoded Federated Learning
Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, …
Federated LearningPrivacy-aware Berrut Approximated Coded Computing applied to general distributed learning
Coded computing is one of the techniques that can be used for privacy protection in Federated Learning. However, most of the constructions used for coded computing work only under the assumption that the computations inv…
Federated LearningStochastic 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…
Federated LearningPrivacy PreservingScalable Federated Unlearning via Isolated and Coded Sharding
Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduce…