针对设备不可靠的联邦学习，提出自适应筛选与缓存机制。
A Robust Federated Learning Framework for Undependable Devices at Scale
- 根据历史行为概率评估设备可靠性，动态选择可信设备参与训练。
- 通过本地缓存保存最新训练状态，减少因中断导致的资源浪费。
- 基于延迟感知策略分发模型，兼顾性能与资源效率，适合大规模移动场景。
在联邦学习系统中，如智能手机等设备常因频繁断网而不可靠。现有框架假设环境可靠，将不可靠设备排除在外，导致模型性能差且资源浪费。本文提出FLUDE，通过分析设备历史行为的概率分布（如成功完成训练的可能性），评估其可靠性，并自适应选择高可靠性设备参与训练。为减少训练阶段的资源浪费，FLUDE在每台设备上维护一个模型缓存，以保留最新训练状态，便于中断后恢复。此外，提出一种延迟感知策略，仅向部分设备分发全局模型，显著降低资源消耗的同时保持模型性能。我们在120部智能手机和NVIDIA Jetson设备上实现FLUDE，实验表明其能有效提升不可靠环境下的模型性能与资源效率。
原文摘要 · Abstract (English)
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks always assume a dependable environment and exclude undependable devices from training, leading to poor model performance and resource wastage. In this paper, we propose FLUDE to effectively deal with undependable environments. First, FLUDE assesses the dependability of devices based on the probability distribution of their historical behaviors (e.g., the likelihood of successfully completing training). Based on this assessment, FLUDE adaptively selects devices with high dependability for training. To mitigate resource wastage during the training phase, FLUDE maintains a model cache on each device, aiming to preserve the latest training state for later use in case local training on an undependable device is interrupted. Moreover, FLUDE proposes a staleness-aware strategy to judiciously distribute the global model to a subset of devices, thus significantly reducing resource wastage while maintaining model performance. We have implemented FLUDE on two physical platforms with 120 smartphones and NVIDIA Jetson devices. Extensive experimental results demonstrate that FLUDE can effectively improve model performance and resource efficiency of FL training in undependable environments.
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