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FedAvgen: Metadata for Model Aggregation In Communication Systems

arXiv 2505.05486 ↗First posted 28 Apr 2025cs.NE cs.AI cs.LGcited by 0

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Abstract (arXiv)

To improve business efficiency and minimize costs, Artificial Intelligence (AI) practitioners have adopted a shift from formulating models from scratch towards sharing pretrained models. The pretrained models are then aggregated into a global model with higher generalization capabilities, which is afterwards distributed to the client devices. This approach is known as federated learning and inherently utilizes different techniques to select the candidate client models averaged to obtain the global model. This approach, in the case of communication systems, faces challenges arising from the existential diversity in device profiles. The multiplicity in profiles motivates our conceptual assessment of a metaheuristic algorithm (FedAvgen), which relates each pretrained model with its weight space as metadata, to a phenotype and genotype, respectively. This parent-child genetic evolution characterizes the global averaging step in federated learning. We then compare the results of our approach to two widely adopted baseline federated learning algorithms like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 38 references that are in the map. First posted on arXiv 28 Apr 2025.

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References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
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  3. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
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  5. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  6. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  7. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  8. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  9. 2019Asynchronous Decentralized SGD with Quantized and Local Updatescited by 35
  10. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  11. 2024Checkpoint Merging via Bayesian Optimization in LLM Pretrainingcited by 27
  12. 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learningcited by 19
4 more
  1. 2021Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Datacited by 12
  2. 2021Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learningcited by 7
  3. 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedbackcited by 6
  4. 2021Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clientscited by 4

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