With the widespread use of surveillance cameras in smart cities [104] and the boom of online video applications powered by 4/5G communication technologies, traditional human inspection is no longer able to accurately monitor the video data generated around the clock, which is not only time-consuming and labor-intensive but also poses the risk of leaking important information (e.g., biometrics and sensitive speech). In contrast, VAD-empowered IoVT applications [54], such as Intelligent Surveillance Systems (IVSS) and automated content analysis platforms, can process massive video streams online and detect events of interest in real-time, sending only noteworthy anomaly parts for human review, significantly reducing data storage and communication costs, and helping to eliminate public concerns about data security and privacy protection. As a result, VAD has gained widespread attention in academia and industry over the last decade and has been used in emerging fields such as information forensics [154], industrial manufacturing [71] in smart cities as well as online content analysis in mobile video applications [153]. VAD extends the data scope of conventional Anomaly Detection (AD) from time series, images, and graphs to video, which not only needs to cope with the endogenous data complexity, but also needs to take into account the computational and communication costs in resource-limited devices [55]. Specifically, the inherent high-dimensional structure of video data, high information density and redundancy, heterogeneity of temporal and spatial patterns, and feature entanglement between foreground targets and background scenes make VAD more challenging than traditional AD tasks at the levels of representation learning and anomaly discrimination [89]. Existing studies [4, 60, 69, 76] have shown that high-performance VAD models need to target the modeling of appearance and motion information, i.e., the difference between regular events and anomalous examples in both spatial and temporal dimensions. In contrast to time series AD that mainly measures periodic temporal patterns of variables, and image AD which only focusing on spatial contextual deviations, VAD needs to extract both discriminative spatial and temporal features from a large amount of redundant information (e.g., repetitive temporal contexts and label-independent data distributions), as well as to learn the differences between normal and anomalous events in terms of their local appearances and global motions [100]. However, video anomalies are ambiguous and subjective [48].

Automated management requires decomposing high-level user requests, such as intents, to an abstraction that the system can understand and execute. This is challenging because even a simple intent requires performing a number of ordered steps. And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand. To tackle these challenges and support automated intent decomposition and execution, we explore the few-shot capability of Large Language Models (LLMs). We propose a pipeline that progressively decomposes intents by generating the required actions using a policy-based abstraction. This allows us to automate the policy execution by creating a closed control loop for the intent deployment. To do so, we generate and map the policies to APIs and form application management loops that perform the necessary monitoring, analysis, planning and execution. We evaluate our proposal with a use-case to fulfill and assure an application service chain of virtual network functions. Using our approach, we can generalize and generate the necessary steps to realize intents, thereby enabling intent automation for application management.

Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data. While most of the existing work focuses on improving the FL model accuracy, in this paper, we focus on the improving the training efficiency, which is often a hurdle for adopting FL in real-world applications. Specifically, we design an efficient FL framework which jointly optimizes model accuracy, processing latency and communication efficiency, all of which are primary design considerations for real implementation of FL. Inspired by the recent success of Multi-Agent Reinforcement Learning (MARL) in solving complex control problems, we present \textit{FedMarl}, an MARL-based FL framework which performs efficient run-time client selection. Experiments show that FedMarl can significantly improve model accuracy with much lower processing latency and communication cost.

Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). However, existing communication schemes often require agents to exchange an excessive number of messages at run-time under a reliable communication channel, which hinders its practicality in many real-world situations. In this paper, we present \textit{Temporal Message Control} (TMC), a simple yet effective approach for achieving succinct and robust communication in MARL. TMC applies a temporal smoothing technique to drastically reduce the amount of information exchanged between agents. Experiments show that TMC can significantly reduce inter-agent communication overhead without impacting accuracy. Furthermore, TMC demonstrates much better robustness against transmission loss than existing approaches in lossy networking environments.

Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving efficient communication among agents has always been an overarching problem in MARL. In this work, we propose Variance Based Control (VBC), a simple yet efficient technique to improve communication efficiency in MARL. By limiting the variance of the exchanged messages between agents during the training phase, the noisy component in the messages can be eliminated effectively, while the useful part can be preserved and utilized by the agents for better performance. Our evaluation using a challenging set of StarCraft II benchmarks indicates that our method achieves $2-10\times$ lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to better collaborate by developing sophisticated strategies.