高效联邦学习与超维度计算（HDC）
Energy Efficient Federated Learning with Hyperdimensional Computing (HDC)
AI总结：
本文提出一种结合超维度计算和差分隐私的联邦学习框架，通过优化资源分配实现高效能耗降低，同时保持高精度和快速收敛。
摘要：
本文研究了在无线边缘网络中实现安全联邦学习（FL）以最小化总能耗的问题，这是分布式大数据分析的关键范式。为解决传统神经网络处理大规模分布式数据时的高计算成本和隐私挑战，我们提出了一种结合超维度计算（HDC）和差分隐私（DP）的联邦学习框架（FL-HDC-DP）。每个边缘设备使用超维度计算进行轻量级本地训练，并应用差分隐私噪声保护传输的模型更新。通过联合优化HDC维度、发射功率和CPU频率，最小化总能耗。开发了高效的混合算法，结合外层枚举搜索HDC维度和内层一维搜索资源分配。仿真结果表明，所提出的框架相比基线方案实现了高达83.3%的能耗降低，同时保持了高精度和更快的收敛速度。
英文摘要：
This paper investigates the problem of minimizing total energy consumption for secure federated learning (FL) in wireless edge networks, a key paradigm for decentralized big data analytics. To tackle the high computational cost and privacy challenges of processing large-scale distributed data with conventional neural networks, we propose an FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. Each edge device employs hyperdimensional computing (HDC) for lightweight local training and applies differential privacy (DP) noise to protect transmitted model updates. The total energy consumption is minimized through a joint optimization of the HDC dimension, transmit power, and CPU frequency. An efficient hybrid algorithm is developed, combining an outer enumeration search for HDC dimensions with an inner one-dimensional search for resource allocation. Simulation results show that the proposed framework achieves up to 83.3% energy reduction compared with baseline schemes, while maintaining high accuracy and faster convergence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
