Multi-Resource Allocation for On-Device Distributed Federated Learning Systems
This work addresses resource optimization for mobile devices in federated learning, though it appears incremental as it builds on existing FL frameworks with specific algorithmic improvements.
The paper tackles the problem of minimizing latency and energy consumption in on-device distributed federated learning systems by proposing a multi-resource allocation scheme, achieving global optimal solutions through decomposition into convex sub-problems and validated via numerical simulations.
This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.