Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
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Abstract (arXiv)
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4X, time efficiency by 8X, and reduces costs by more than 2X compared to extant cloud-based static methodologies.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 59 references that are in the map. First posted on arXiv 16 Apr 2022.
Builds on
20 of 59 references.
References · 20
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8 more
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.