arXiv:2605.24418v1 Announce Type: new Abstract: Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, identical architectures, and centralized aggregation, which fails when hospitals have unequal compute resources. We propose capacity-aware coordination: measure each hospital's throughput, assign capacity-appropriate architectures (MobileNetV3-Small, EfficientNet-B0, ResNet-50), and combine predictions via weighted ensemble. Weak and strong hospitals can participate without forcing uniform architectur
The increasing demand for privacy-preserving AI in sensitive sectors like healthcare, combined with the practical complexities of heterogeneous computing environments, drives the need for advanced federated learning solutions.
This development addresses a critical barrier to scaled federated learning adoption by enabling diverse participants to contribute effectively, unlocking new data sets and compute capacities for AI model training.
The ability to run federated learning on heterogeneous hardware ensures broader participation and more robust models, moving beyond the idealized assumptions of prior federated AI frameworks.
- · Hospitals with limited compute
- · Federated AI platform providers
- · Medical imaging diagnostics
- · Privacy-focused AI applications
- · Centralized AI training models
- · AI solutions requiring homogeneous infrastructure
More widespread and inclusive adoption of federated learning in privacy-sensitive domains.
Accelerated development of AI models for medical diagnostics and other privacy-constrained applications.
Enhanced data sovereignty for institutions as they can contribute to AI development without exposing raw data to central aggregators.
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Read at arXiv cs.LG