mlsanityData to 7 Oct 2026

Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0

arXiv 2507.07613 ↗First posted 10 Jul 2025cs.LGcited by 1

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Abstract (arXiv)

Federated Learning offers privacy-preserving collaborative intelligence but struggles to meet the sustainability demands of emerging IoT ecosystems necessary for Society 5.0-a human-centered technological future balancing social advancement with environmental responsibility. The excessive communication bandwidth and computational resources required by traditional FL approaches make them environmentally unsustainable at scale, creating a fundamental conflict with green AI principles as billions of resource-constrained devices attempt to participate. To this end, we introduce Sparse Proximity-based Self-Federated Learning (SParSeFuL), a resource-aware approach that bridges this gap by combining aggregate computing for self-organization with neural network sparsification to reduce energy and bandwidth consumption.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 48 references that are in the map. First posted on arXiv 10 Jul 2025.

Builds on

19 of 48 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  4. 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 507
  5. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  7. 2017Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Sciencecited by 325
  8. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 259
  9. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 150
  10. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  11. 2020Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-artcited by 19
  12. 2024A Survey on Federated Learning in Intelligent Transportation Systemscited by 9
7 more
  1. 2022Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modellingcited by 8
  2. 2016A Higher-order Calculus of Computational Fieldscited by 6
  3. 2017Engineering Resilient Collective Adaptive Systems by Self-Stabilisationcited by 6
  4. 2024Proximity-based Self-Federated Learningcited by 5
  5. 2025FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learningcited by 5
  6. 2025ProFed: a Benchmark for Proximity-based non-IID Federated Learningcited by 5
  7. 2024Unveiling the Power of Sparse Neural Networks for Feature Selectioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Phyelds: A Pythonic Framework for Aggregate Computingcited by 1