# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 2 Related Work
While the previously mentioned studies demonstrate the feasibility and efficiency of performing AD inference on edge devices, the majority of the existing work assumes that model training is performed offline, on more powerful hardware, before deployment. This approach stems from the limited computational, memory, and energy resources available on embedded platforms, which often make full training impractical. Nonetheless, recent studies have demonstrated that, with appropriate strategies, on-device training of deep neural networks is feasible even on highly resource-constrained MCUs. TinyTrain [31] introduces a task-adaptive sparse update mechanism that selectively fine-tunes layers or channels, significantly reducing memory and computation overhead while maintaining accuracy and fast training. Similarly, [PERSON] et al. [32] propose a fully quantized training framework with dynamic partial gradient updates, allowing efficient training on Cortex-M MCUs with substantial reductions in memory and latency. Focusing on unsupervised approaches, [PERSON] et al. [33] present TinyOL, a system for online training of AEs directly on streaming data in IoT devices, supporting both supervised and unsupervised setups. [PERSON] et al. [34] design a family of highly compact convolutional AEs called OutlierNets, with as few as 686 parameters, showing that accurate AD is possible with extremely lightweight models deployable on devices with limited RAM. As edge intelligence advances, FL has become an increasingly popular strategy for enabling collaborative model training across distributed IoT devices without sharing raw data. This paradigm, often referred to as TinyFL when applied to MCUs and resource-constrained nodes, supports privacy preservation and decentralized learning. [PERSON] et al. [35] provide a comprehensive survey of lightweight FL approaches, categorizing key techniques and identifying open challenges relevant to embedded deployments. Several recent works explore the feasibility of FL on edge platforms. For instance, [PERSON] et al. [36] present TinyFedTL, the first open-source implementation of FL on ultra-constrained IoT devices with <1 MB memory, showing that federated transfer learning is feasible and effective even under severe memory and processing constraints. [PERSON] et al. [37] propose combining FL with transfer learning to enable on-board training for classification and regression tasks on IoT devices, evaluating performance against TensorFlow Lite-based solutions. [PERSON] et al. [38] demonstrate an end-to-end FL pipeline for digit recognition using a camera-equipped ESP32 MCU, highlighting that on-device training combined with FL can maintain high accuracy (97.01%) while keeping communication energy costs minimal. [PERSON] et al. [39] further extend this direction with TinyReptile, a federated meta-learning approach for TinyML devices, enabling fast adaptation of neural networks on resource-constrained devices such as the Raspberry Pi 4 and Cortex-M4 MCU with only 256 KB of RAM. Recent efforts have also explored combining FL with autoencoder-based AD on the edge. [PERSON] et al. [40] propose a deep autoencoder for FL, a privacy-preserving framework that reduces training time via non-iterative distributed training. [PERSON] et al. [41] design a federated 5G-edge architecture for Industrial IoT, leveraging LSTM-based AEs for AD. Similarly, [PERSON] et al. [42] introduce Edge-FLGuard, integrating AEs and LSTMs in a federated pipeline to enable low-latency on-device inference for cybersecurity in IoT networks. [PERSON] et al. [43] evaluate both supervised and unsupervised DL models for intrusion detection, demonstrating that an FL-trained AE achieves the best performance across nine IoT devices using the N-BaloT dataset. [PERSON] et al. [44] present WAFL-Autoencoder, a fully distributed FL scheme based on device-to-device collaboration without centralized coordination.