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Life sciences · Preprint
arXiv · August 10, 2026
Posted before peer review. The findings may change or fail to hold.
FedA2L is a proposed algorithmic innovation for decentralized federated learning that dynamically adjusts layer-wise learning rates to improve convergence speed and communication efficiency under data heterogeneity. The work is a technical methods preprint demonstrating simulation-based improvements without peer review, real-world validation, or applicability beyond computational optimization.
Preprint. Intervention: FedA2L: adaptive layer-wise learning rate adjustment method based on model divergence signals and local update intensity. Compared with: Vanilla DFL and scheduler-based baseline methods.
FedA2L achieves up to 4.94 times faster convergence than vanilla DFL Reduces communication rounds by up to 59% compared to scheduler-based baselines Exhibits resilience to severe data heterogeneity, larger network sizes, and sparse topologies
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This is an unrefereed technical preprint proposing a novel algorithmic method for federated learning, demonstrating computational improvements in simulation but lacking peer review, clinical relevance, or human validation.
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Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from convergence inefficiency under data heterogeneity due to the use of a uniform learning rate (LR) that ignores layer-specific optimization needs. Foundational layers are responsible for maintaining network consensus, while specialized layers adapt to local data characteristics, leading to conflicting gradients and degraded performance under non-IID conditions. To address this fundamental tension, this work introduces FedA2L, a method that dynamically adjusts layer-wise LRs based on model divergence signals. By leveraging local update intensity and network consensus constraints, FedA2L seamlessly integrates into existing DFL protocols without additional communication or coordination. Extensive evaluations across DFL algorithms, various model architectures, and datasets demonstrate that FedA2L achieves up to 4.94 times faster convergence than vanilla DFL and reduces communication rounds by up to 59% compared to scheduler-based baselines. Furthermore, FedA2L exhibits resilience to severe data heterogeneity, larger network sizes, and sparse topologies, reducing communication overhead and establishing it as a versatile optimization tool for resource-constrained or large-scale distributed learning in edge and IoT deployments. The code is released at https://github.com/nclabteam/FedA2L.
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