通过稀疏化与自组织计算，降低物联网设备协同学习的能耗和带宽消耗。
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0
- 基于邻近性自组织，结合神经网络稀疏化减少资源占用。
- 在资源受限设备上实现高效通信与计算，支持大规模可持续协作。
- 适合关注绿色AI与边缘智能的开发者与研究者。
联邦学习虽能实现隐私保护的协同智能，但在面向未来社会5.0的物联网生态系统中，其高昂的通信带宽与计算资源消耗难以满足可持续发展需求。传统方法在数十亿资源受限设备参与时，违背绿色AI原则。为此，我们提出稀疏邻近自联邦学习（SParSeFuL），通过聚合计算实现自组织，并结合神经网络稀疏化，显著降低能量与带宽开销，有效弥合技术需求与可持续性之间的鸿沟。
原文摘要 · Abstract (English)
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.
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