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.

翻译：联邦学习虽能实现隐私保护的协同智能，却难以满足社会5.0——这一平衡社会进步与环境责任、以人为中心的技术未来——所需新兴物联网生态系统的可持续发展要求。传统联邦学习方法对通信带宽与计算资源的过度消耗，使其在大规模部署时缺乏环境可持续性，当数十亿资源受限设备试图参与时，便与绿色人工智能原则产生根本性冲突。为此，我们提出基于稀疏近邻的自联邦学习方法，该资源感知型框架通过融合自组织聚合计算与神经网络稀疏化技术，有效降低能耗与带宽需求，从而弥合上述鸿沟。