Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost.

The issue of potential privacy leakage during centralized AIs model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally without uploading their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The selection policy is critical to an FL process in terms of round efficiency, the final models quality as well as fairness. In this paper, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C2MAB-based method is proposed for estimating the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. In addition to the theoretical results, more empirical ones can be derived from our real training experiments on public datasets.

Network intrusion detection systems (NIDS) are essential tools in ensuring network information security, and neural networks have become an increasingly popular solution for NIDS. However, with the gradual complexity of the network environment, the existing solutions using the conventional neural network cannot make full use of the rich information in the network traffic data due to its single structure. More importantly, this will lead to the existing NIDS have incomplete knowledge of the intrusion detection domain, and making it unable to achieve a high detection rate and good stability in the new environment. In this paper, we take a step forward and extract the different level features from the network connection, rather than a long feature vector used in the traditional approach, which can process feature information separately more efficiently. And further, we propose multimodal-sequential intrusion detection approach with special structure of hierarchical progressive network, which is supported by multimodal deep auto encoder (MDAE) and LSTM technologies. By design the special structure of hierarchical progressive network, our approach can efficiently integrate the different level features information within a network connection and automatically learn temporal information between adjacent network connections at the same time. Based on the three benchmark datasets from 1999 to 2017, including NSL-KDD, UNSW-NB15, and CICIDS 2017, we investigated the performance of our proposed approach on the task of detecting attacks within modern network. The experimental results show that the average accuracy of this method is 94% in binary classification and 88% in multi-class classification, which is at least 2% and 4% super than other methods respectively, and demonstrated that our model has excellent stability. Moreover, we further explore the multimodality and complementarity in traffic data, the experimental results show that the performance of detection model can be further improved in the range 2% to 5% when using our MDAE model to process the features of traffic data.

Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes, and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality in such circumstances. In this article, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12×) and reducing end device energy consumption (by up to 58 percent).

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency.

As data centers are consuming massive amount of energy, improving the energy efficiency of cloud computing has emerged as a focus of research. However, it is challenging to reduce energy consumption while maintaining system performance without increasing the risk of Service Level Agreement violations. Most of the existing consolidation approaches for virtual machines (VMs) consider system performance and Quality of Service (QoS) metrics as constraints, which usually results in large scheduling overhead and impossibility to achieve effective improvement in energy efficiency without sacrificing some system performance and cloud service quality. In this paper, we first define the metrics of peak power efficiency and optimal utilization for heterogeneous physical machines (PMs). Then we propose Peak Efficiency Aware Scheduling (PEAS), a novel strategy of VM placement and reallocation for achieving dual improvement in performance and energy conservation from the perspective of server clusters. PEAS allocates and reallocates VMs in an on-line manner and always attempts to maintain PMs working in their peak power efficiency via VM consolidation. Extensive experiments on Cloudsim show that PEAS outperforms several energy-aware consolidation algorithms with regard to energy consumption, system performance as well as multiple QoS metrics.