Federated learning (FL) is a decentralized learning paradigm widely adopted in resource-constrained Internet of Things (IoT) environments. These devices, typically relying on TinyML models, collaboratively train global models by sharing gradients with a central server while preserving data privacy. However, as data heterogeneity and task complexity increase, TinyML models often become insufficient to capture intricate patterns, especially under extreme non-IID (non-independent and identically distributed) conditions. Moreover, ensuring robustness against malicious clients and poisoned updates remains a major challenge. Accordingly, this paper introduces RIFLE - a Robust, distillation-based Federated Learning framework that replaces gradient sharing with logit-based knowledge transfer. By leveraging a knowledge distillation aggregation scheme, RIFLE enables the training of deep models such as VGG-19 and Resnet18 within constrained IoT systems. Furthermore, a Kullback-Leibler (KL) divergence-based validation mechanism quantifies the reliability of client updates without exposing raw data, achieving high trust and privacy preservation simultaneously. Experiments on three benchmark datasets (MNIST, CIFAR-10, and CIFAR-100) under heterogeneous non-IID conditions demonstrate that RIFLE reduces false-positive detections by up to 87.5%, enhances poisoning attack mitigation by 62.5%, and achieves up to 28.3% higher accuracy compared to conventional federated learning baselines within only 10 rounds. Notably, RIFLE reduces VGG19 training time from over 600 days to just 1.39 hours on typical IoT devices (0.3 GFLOPS), making deep learning practical in resource-constrained networks.

翻译：联邦学习（FL）是一种广泛应用于资源受限物联网（IoT）环境的去中心化学习范式。这些设备通常依赖TinyML模型，通过与中央服务器共享梯度来协作训练全局模型，同时保护数据隐私。然而，随着数据异构性和任务复杂性的增加，TinyML模型往往难以捕捉复杂模式，尤其是在极端非独立同分布（non-IID）条件下。此外，确保对恶意客户端和中毒更新的鲁棒性仍然是一个重大挑战。为此，本文提出了RIFLE——一种基于蒸馏的鲁棒联邦学习框架，它用基于logit的知识传递替代了梯度共享。通过利用知识蒸馏聚合方案，RIFLE能够在受限的IoT系统中训练如VGG-19和Resnet18等深度模型。此外，基于Kullback-Leibler（KL）散度的验证机制量化了客户端更新的可靠性，而无需暴露原始数据，从而同时实现了高可信度和隐私保护。在异构非IID条件下对三个基准数据集（MNIST、CIFAR-10和CIFAR-100）的实验表明，与传统的联邦学习基线相比，RIFLE在仅10轮训练内将误报检测降低了高达87.5%，将中毒攻击缓解能力提升了62.5%，并实现了高达28.3%的准确率提升。值得注意的是，在典型IoT设备（0.3 GFLOPS）上，RIFLE将VGG19的训练时间从超过600天缩短至仅1.39小时，使得深度学习在资源受限网络中变得切实可行。