Federated learning (FL) is a distributed machine learning framework where multiple clients collaboratively train a model without sharing their data. Despite advancements, traditional FL methods encounter challenges including communication overhead, extended latency, and slow convergence. To address these issues, this paper introduces Anchor-HHFL, a novel approach that combines the strengths of synchronous and asynchronous FL. Anchor-HHFL employs multi-tier edge servers which conduct partial model aggregation and reduce the frequency of communication with the central server. Anchor-HHFL implements a novel divergence control method through hierarchical pullback. It orchestrates the sequence of each client's stochastic gradient descent (SGD) updates to pull the locally trained models towards an anchor model, ensuring alignment and minimizing divergence. Simultaneously, a secondary process collects client models without disrupting their ongoing local computations and transmits them to edge servers, thereby overlapping computation with communication, substantially enhancing the training speed. Additionally, to effectively handle asynchronous updates across clusters, Anchor-HHFL uses a heuristic weight assignment for global aggregation, weighting clients’ updates based on the degree of their divergence from the global model. Extensive experiments on MNIST and CIFAR-10 datasets demonstrate Anchor-HHFL's superiority, achieving up to$3 \times$faster convergence and higher test accuracy compared to the baselines.

Federated Learning is particularly challenging in IoT environments, where edge and cloud nodes have imbalanced computation capacity and networking bandwidth. The main scalability barrier in distributed stochastic gradient descent-based machine learning frameworks is the communication overhead from frequent model parameter exchanges between workers and the central server. One way to reduce this overhead is by employing constant and periodic averaging, which sends model parameters to the server after a few iterations of local updates from workers. However, investigations have shown that the optimal communication period for balancing communication and convergence is not constant. Although some studies have explored the effectiveness of federated learning with a constant period, dynamically adjusting the period for optimal convergence remains under-explored. To address this, we investigate the impact of the period on global model convergence and propose an adaptive period control mechanism (AdaPC). This mechanism adaptively adjusts the aggregation period of the federated learning framework to achieve fast convergence with minimal communication. Our theoretical and empirical findings demonstrate that our proposed solution achieves faster convergence, lower final training loss, and minimized communication overhead compared to the constant period averaging strategy and other existing solutions.

We address privacy and latency issues in edge-cloud computing environments where the neural network training is centralized. This paper considers the scenario where the edge devices are the only data sources for the deep learning model to be trained on the central server. Improper access to the massive amounts of data generated by edge devices could lead to privacy concerns. As a result, existing solutions for preserving privacy and reducing network latency in the edge environment rely on auxiliary datasets with no privacy risks or pre-trained models to build the client side feature extractor. However, finding auxiliary datasets or pre-trained models is not always guaranteed and may be challenging. To bridge this gap and eliminate the reliance on auxiliary datasets or pre-trained models of existing solutions, this paper presents DeepGuess, a privacy-preserving and latency-aware deep-learning framework. DeepGuess introduces a new learning mechanism enabled by the AutoEncoder architecture: inductive learning. With inductive learning, sensitive data stays on devices and is not explicitly sent to the central server to engage in back-propagations. To further enhance privacy, we propose a new local differential privacy algorithm that allows edge devices to apply random noise to features extracted from their sensitive data before being transferred to the non-trusted central server. The experimental evaluation of DeepGuess with various datasets and in a real-world scenario shows that our solution achieves comparable or even higher accuracy than existing solutions while reducing data transfer over the network by more than 50%.

Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common features across sub-tasks to improve training speed and accuracy. The experimental results on several popular datasets demonstrate the effectiveness of our approach in speeding up inferences while processing most of the input data with a low error rate.

Given the limited bandwidth available in distributed camera systems, it is nearly impossible for cameras to transmit their entire feed to the server in real time. Furthermore, as the number of camera units increases, the processing overheads on the server also increase, resulting in excessive latencies. This article introduces SmartFilter, a new Edge-to-Cloud filtering solution for video analytics. SmartFilter exploits the feedbacks from the running server-side application to filter directly on the camera, frames that are likely to produce the same application result as the previously offloaded ones. Because of its unique filtering mechanism, SmartFilter improves the system’s throughput, latency, and network usage and reduces the server’s processing overhead while maintaining the overall accuracy. SmartFilter is typically a fast and lightweight binary classifier that examines changes within frames to decide when these changes are significant enough to alter the application output. Experiments with various video data sets and in a real-world scenario demonstrate that our solution can achieve 40 FPS on a commodity camera while delivering a filtering efficiency of more than 90%.