mlsanityData to 14 Oct 2026

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

arXiv 2211.02906 ↗First posted 5 Nov 2022cs.AI cs.DC cs.LGcited by 7

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Abstract (arXiv)

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each dataset over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The Genetic algorithm (GA) is used to solve the multi-objective optimization problem due to its evolutionary strategy. The performed experiments using the Mobile Data Challenge (MDC) dataset and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 7 of its 58 references that are in the map. First posted on arXiv 5 Nov 2022.

Builds on

7 of 58 references.

References · 7

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Towards Federated Learning at Scale: System Designcited by 832
  3. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
  4. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  5. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  6. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  7. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 39

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Cited by year

Citing papers per year: 2023: 1; 2024: 6.

7 papers.

  1. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  2. 2024CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authenticationcited by 2
  3. 2024Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?cited by 2
  4. 2024Trust Driven On-Demand Scheme for Client Deployment in Federated Learningcited by 1
  5. 2024On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learningcited by 1
  6. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  7. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0