Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge Learning
This work addresses efficiency challenges in distributed edge learning for applications like IoT and mobile computing, though it is incremental as it builds on existing asynchronous federated learning methods.
The paper tackles the problem of minimizing gradient staleness in asynchronous federated learning on wireless edge nodes with heterogeneous capacities, proposing an adaptive task allocation scheme that reduces staleness and offers better accuracy than synchronous and equal allocation baselines.
This paper proposes a scheme to efficiently execute distributed learning tasks in an asynchronous manner while minimizing the gradient staleness on wireless edge nodes with heterogeneous computing and communication capacities. The approach considered in this paper ensures that all devices work for a certain duration that covers the time for data/model distribution, learning iterations, model collection and global aggregation. The resulting problem is an integer non-convex program with quadratic equality constraints as well as linear equality and inequality constraints. Because the problem is NP-hard, we relax the integer constraints in order to solve it efficiently with available solvers. Analytical bounds are derived using the KKT conditions and Lagrangian analysis in conjunction with the suggest-and-improve approach. Results show that our approach reduces the gradient staleness and can offer better accuracy than the synchronous scheme and the asynchronous scheme with equal task allocation.