# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 2 Related Work  
Anomaly detection has been extensively studied in recent years, with machine learning methods demonstrating strong performance across a range of domains. A comprehensive survey by [PERSON] et al. [15] highlights the success of neural network-based approaches, particularly autoencoders, which are the most frequently used for outlier detection. In a recent review, [PERSON] et al. [16] provide a broad overview of anomaly detection techniques, covering both traditional ML and deep learning-based methods and outlining their suitability across various application domains. Various recent studies have further advanced the field by proposing novel AD techniques. [PERSON] et al. [17] propose a Bayesian deep learning framework that leverages model uncertainty to identify distributional shifts, achieving robust detection under noise, adversarial attacks, and label perturbations. [PERSON] et al. [18] enhance traditional autoencoder-based AD by incorporating Mahalanobis distance in the latent space, demonstrating improved out-of-distribution detection across varying bottleneck sizes. Meanwhile, [PERSON] et al. [19] explore anomaly detection as a classification problem, showing that, even with very few labeled anomalies, classifiers can effectively separate normal and anomalous data points. Complementing these works, [PERSON] et al. [20] emphasize the growing relevance of edge computing in AD systems for future 6G-enabled IoT applications, where ultra-low latency and distributed intelligence will be essential. As interest in deep learning grows, researchers are increasingly targeting embedded platforms and low-power hardware for running ML models directly on the edge. This shift has led to the rise of TinyML [1], which focuses on deploying ML models on constrained edge devices. A recent survey by [PERSON] et al. [21] emphasizes the multidisciplinary nature of TinyML, highlighting its critical role in modern industrial revolutions and its impact on applications such as smart cities and robotics. Similarly, [PERSON] et al. [22]underline the growing need to move ML from the cloud to the edge, where IoT devices must process massive data streams with minimal latency and power usage. To address the challenges of limited compute, memory, and energy resources on edge devices, various optimization techniques have been explored. For example, weight pruning and quantization methods have been proposed to reduce model size and energy usage during inference [23; 24]. MCUNet [25] exemplifies this trend by combining neural architecture search (TinyNAS) with a memory-efficient runtime (TinyEngine), enabling ImageNet-scale inference on microcontrollers. Collectively, these efforts showcase the growing potential of deploying ML at the extreme edge using TinyML principles. Autoencoders have become a widely adopted approach for anomaly detection on edge devices due to their unsupervised nature and low inference cost. [PERSON] et al. [26] proposed a squeezed convolutional variational autoencoder, designed for real-time AD within Industrial IoT environments. Their approach prioritizes model compression and low-latency inference, making it suitable for edge deployment. Similarly, [PERSON] et al. [27] demonstrate autoencoder-based fault detection for rotary machines using real-time vibration data, enabling interpretable alerts for proactive maintenance. [PERSON] et al. [28] also explore this domain by deploying a compact AE directly on an ESP32 device, enabling continuous fully local monitoring of motor vibrations without requiring cloud connectivity. [PERSON] et al. [29] deploy both fully connected and convolutional autoencoders on ultra-low-power STM32L4 MCUs for structural health monitoring, achieving efficient real-time inference at the edge while minimizing network load. Finally, [PERSON] and [PERSON] [30] propose a hybrid approach in which AEs are used for local anomaly detection on wireless sensor nodes, while model updates are handled by the cloud at configurable intervals, reducing communication overhead in distributed IoT systems.