FTTE让边缘设备在资源有限下高效协同训练模型
FTTE: Enabling Federated and Resource-Constrained Deep Edge Intelligence
- 采用稀疏参数更新与时效性加权聚合，减少通信开销
- 在500个客户端、90%延迟设备下实现81%更快收敛
- 适合大规模异构边缘设备部署，尤其资源受限场景
联邦学习（FL）可在保护数据隐私的前提下实现分布式设备协同训练，但部署在资源受限的边缘节点时仍面临内存、能耗和通信带宽不足的挑战。传统同步与异步方法在异构大规模网络中易受慢节点影响，导致延迟和收敛缓慢。本文提出FTTE（联邦极小训练引擎），一种新型半异步联邦学习框架，创新性地结合稀疏参数更新与基于更新时间戳和方差的过时加权聚合策略。在多种模型与数据分布下的广泛实验表明，FTTE相比同步联邦平均（FedAVG）实现81%更快收敛、80%更低的本地内存占用、69%更少的通信负载；同时在极端场景（最多500个客户端、90%延迟设备）下，精度与半异步方法（如FedBuff）相当或更高。这些结果确立了FTTE作为首个可实际部署于异构且资源受限边缘设备的规模化联邦学习解决方案。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy, but deployment on resource-constrained edge nodes remains challenging due to limited memory, energy, and communication bandwidth. Traditional synchronous and asynchronous FL approaches further suffer from straggler induced delays and slow convergence in heterogeneous, large scale networks. We present FTTE (Federated Tiny Training Engine),a novel semi-asynchronous FL framework that uniquely employs sparse parameter updates and a staleness-weighted aggregation based on both age and variance of client updates. Extensive experiments across diverse models and data distributions - including up to 500 clients and 90% stragglers - demonstrate that FTTE not only achieves 81% faster convergence, 80% lower on-device memory usage, and 69% communication payload reduction than synchronous FL (eg.FedAVG), but also consistently reaches comparable or higher target accuracy than semi-asynchronous (eg.FedBuff) in challenging regimes. These results establish FTTE as the first practical and scalable solution for real-world FL deployments on heterogeneous and predominantly resource-constrained edge devices.
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