Driven by the ever-growing volume and decentralized nature of data, coupled with the escalating size of modern models, distributed deep learning (DDL) has been entrenched as the preferred paradigm for training. However, frequent synchronization of DL models, encompassing millions to many billions of parameters, creates a communication bottleneck, severely hindering scalability. Worse yet, DDL algorithms typically waste valuable bandwidth, and make themselves less practical in bandwidth-constrained federated settings, by relying on overly simplistic, periodic, and rigid synchronization schedules. To address these shortcomings, we propose Federated Dynamic Averaging (FDA), a communication-efficient DDL strategy that dynamically triggers synchronization based on the value of the model variance. Through extensive experiments across a wide range of learning tasks we demonstrate that FDA reduces communication cost by orders of magnitude, compared to both traditional and cutting-edge communication-efficient algorithms. Remarkably, FDA achieves this without sacrificing convergence speed - in stark contrast to the trade-offs encountered in the field. Additionally, we show that FDA maintains robust performance across diverse data heterogeneity settings.

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