Online Scheduling of Federated Learning with In-Network Aggregation and Flow Routing

Continuously orchestrating in-network model aggregations for federated learning faces fundamental challenges such as the combinatorial nature of traffic reduction, the dynamic trade-offs between system overhead and model convergence, and the unpredictable inputs from uncertain system environments. In this work, we model a nonlinear mixed-integer program to optimize the long-term total cost of federated learning computation overhead, traffic reduction, network delay, and programmable switch reconfigurations over time. To attack the lexicographic minimax, submodular, and online nature of this problem, we propose a polynomial-time algorithmic framework to judiciously designate the timing of reconfigurations, while designing and invoking a linearized transformation for selecting routing paths, a greedy sub-algorithm for selecting aggregation locations, and an online learning sub-algorithm for controlling federated learning convergence. We demonstrate our rigorous mathematical insights behind our algorithms, and prove the competitive ratio as the performance guarantee. Using trace-driven evaluations, we have validated our approach's superiority over existing methods.