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

arXiv: https://arxiv.org/abs/2507.07613 · HTML: https://mlsanity.com/paper/2507.07613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science](https://mlsanity.com/paper/1707.04780/) (2017)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching](https://mlsanity.com/paper/2203.12285/) (2022)
- [Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art](https://mlsanity.com/paper/2002.10610/) (2020)
- [A Survey on Federated Learning in Intelligent Transportation Systems](https://mlsanity.com/paper/2403.07444/) (2024)
- [Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling](https://mlsanity.com/paper/2201.03473/) (2022)
- [A Higher-order Calculus of Computational Fields](https://mlsanity.com/paper/1610.08116/) (2016)
- [Engineering Resilient Collective Adaptive Systems by Self-Stabilisation](https://mlsanity.com/paper/1711.08297/) (2017)
- [Proximity-based Self-Federated Learning](https://mlsanity.com/paper/2407.12410/) (2024)
- [FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2502.08577/) (2025)
- [ProFed: a Benchmark for Proximity-based non-IID Federated Learning](https://mlsanity.com/paper/2503.20618/) (2025)
- [Unveiling the Power of Sparse Neural Networks for Feature Selection](https://mlsanity.com/paper/2408.04583/) (2024)

## Built on by (most cited first, 1 of 1)

- [Phyelds: A Pythonic Framework for Aggregate Computing](https://mlsanity.com/paper/2603.29999/) (2026)

## 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.