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Not AvailablDeploying monocular depth estimation on resource-constrained edge devices is a significant challenge, particularly when attempting to perform both training and inference concurrently. Current lightweight, self-supervised approaches typically rely on complex frameworks that are hard to implement and deploy in real-world settings. To address this gap, we introduce the first framework for Lightweight Training and Inference (LITI) that combines ready-to-deploy models with streamlined code and fully functional, parallel training and inference pipelines. Our experiments show various models being deployed for inference, training, or both inference and training, leveraging inputs from a real-time RGB camera sensor. Thus, our framework enables training and inference on resource-constrained edge devices for complex applications such as depth estimation. 

Federated continual learning is a decentralized approach that enables edge devices to continuously learn new data, mitigating catastrophic forgetting while collaboratively training a global model. However, existing state-of-the-art approaches in federated continual learning focus primarily on learning continuously to classify discrete sets of images, leaving dense regression tasks such as depth estimation unaddressed. Furthermore, autonomous agents that use depth estimation to explore dynamic indoor environments inevitably encounter spatial and temporal shifts in data distributions. These shifts trigger a phenomenon called spatio-temporal catastrophic forgetting, a more complex and challenging form of catastrophic forgetting. In this paper, we address the fundamental research question: “Can we mitigate spatiotemporal catastrophic forgetting in federated continual learning for depth estimation in dynamic indoor environments?”. To address this question, we propose Local Online and Continual Adaptation (LOCA), the first approach to address spatio-temporal catastrophic forgetting in dynamic indoor environments. LOCA relies on two key algorithmic innovations: online batch skipping and continual local aggregation. Our extensive experiments show that LOCA mitigates spatio-temporal catastrophic forgetting and improves global model performance, while running on-device up to 3.35× faster and consuming 3.13× less energy compared to state-of-the-art. Thus, LOCA lays the groundwork for scalable autonomous systems that adapt in real time to learn private and dynamic indoor environments. 

Hierarchical Federated Learning (HFL) has shown great promise over the past few years, with significant improvements in communication efficiency and overall performance. However, current research for HFL predominantly centers on supervised learning. This focus becomes problematic when dealing with semi-supervised learning, particularly under non-IID scenarios. In order to address this gap, our paper critically assesses the performance of straightforward adaptations of current state-of-the-art semi-supervised FL (SSFL) techniques within the HFL framework. We also introduce a novel clustering mechanism for hierarchical embeddings to alleviate the challenges introduced by semi-supervised paradigms in a hierarchical setting. Our approach not only provides superior accuracy, but also converges up to 5.11× faster, while being robust to non-IID data distributions for multiple datasets with negligible communication overhead 

A central challenge in machine learning deployment is maintaining accurate and updated models as the deployment environment changes over time. We present a hardware/software framework for simultaneous training and inference for monocular depth estimation on edge devices. Our proposed framework can be used as a hardware/software co-design tool that enables continual and online federated learning on edge devices. Our results show real-time training and inference performance, demonstrating the feasibility of online learning on edge devices. 

This paper presents a new hardware prototype to explore how centralized and hierarchical federated learning systems are impacted by real-world devices distribution, availability, and heterogeneity. Our results show considerable learning performance degradation and wasted energy during training when users mobility is accounted for. Hence, we provide a prototype that can be used as a design exploration tool to better design, calibrate and evaluate FL systems for real-world deployment. 

The recent developments in Federated Learning (FL) focus on optimizing the learning process for data, hardware, and model heterogeneity. However, most approaches assume all devices are stationary, charging, and always connected to the Wi-Fi when training on local data. We argue that when real devices move around, the FL process is negatively impacted and the device energy spent for communication is increased. To mitigate such effects, we propose a dynamic community selection algorithm which improves the communication energy efficiency and two new aggregation strategies that boost the learning performance in Hierarchical FL (HFL). For real mobility traces, we show that compared to state-of-the-art HFL solutions, our approach is scalable, achieves better accuracy on multiple datasets, converges up to 3.88× faster, and is significantly more energy efficient for both IID and non-IID scenarios. 

This paper presents a hardware prototype and a framework for a new communication-aware model compression for distributed on-device inference. Our approach relies on Knowledge Distillation (KD) and achieves orders of magnitude compression ratios on a large pre-trained teacher model. The distributed hardware prototype consists of multiple student models deployed on Raspberry-Pi 3 nodes that run Wide ResNet and VGG models on the CIFAR10 dataset for real-time image classification. We observe significant reductions in memory footprint (50×), energy consumption (14×), latency (33×) and an increase in performance (12×) without any significant accuracy loss compared to the initial teacher model. This is an important step towards deploying deep learning models for IoT applications.