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Federated Learning (FL) is a distributed learning paradigm that cancoordinate heterogeneous edge devices to perform model training without sharingprivate data. While prior works have focused on analyzing FL convergence withrespect to hyperparameters like batch size and aggregation frequency, the jointeffects of adjusting these parameters on model performance, training time, andresource consumption have been overlooked, especially when facing dynamic datastreams and network characteristics. This paper introduces novel analyticalmodels and optimization algorithms that leverage the interplay between batchsize and aggregation frequency to navigate the trade-offs among convergence,cost, and completion time for dynamic FL training. We establish a newconvergence bound for training error considering heterogeneous datasets acrossdevices and derive closed-form solutions for co-optimized batch size andaggregation frequency that are consistent across all devices. Additionally, wedesign an efficient algorithm for assigning different batch configurationsacross devices, improving model accuracy and addressing the heterogeneity ofboth data and system characteristics. Further, we propose an adaptive controlalgorithm that dynamically estimates network states, efficiently samplesappropriate data batches, and effectively adjusts batch sizes and aggregationfrequency on the fly. Extensive experiments demonstrate the superiority of ouroffline optimal solutions and online adaptive algorithm.

DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset