Coded Computing for Federated Learning at the Edge

By Saurav Prakash, S. Dhakal, Mustafa Riza Akdeniz, Amir Salman Avestimehr, Nageen Himayat. 2020

11 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

FedML: A Research Library and Benchmark for Federated Machine Learning (2020, positive): “To tackle the challenge of system constraints, researchers apply sparsification and quantization techniques to reduce the communication overheads and computation costs during the training process [6, 7, 8, 9, 10, 11, 12, 13, 14].”