Short video platforms face significant challenges in efficiently delivering diverse video analytics to a concurrent user base. These analytics impose substantial storage and computation demands on edge devices, while offloading tasks to the cloud incurs high data transmission overhead. Edge-cloud collaborative computing, which distributes workloads between edge devices and the cloud, offers a promising solution. However, applying this paradigm to short video platforms encounters efficiency and scalability hurdles due to the limited memory capacity of commercial smartphones, the large volume of intermediate analytics data, and the highly dynamic workloads driven by extensive user activity. This paper introduces FaaSVA, a Function-as-a-Service (FaaS)-based edge-cloud video analytics system for short video platforms. FaaSVA enables efficient and scalable video analytics by integrating edge-cloud collaboration with serverless computing. On the edge, it implements a re-utilizing memory provisioning mechanism to allocate, release and reclaim memory for video frames, reducing intermediate data and minimizing the memory footprint on smartphones. On the cloud, a serverless framework supports rapid and elastic adaptation to fluctuating workloads. The system abstracts video analytics functions and utilizes a dynamic, workload-aware Directed Acyclic Graph (DAG) partitioning algorithm to intelligently schedule functions to optimal execution nodes, balancing computation and communication. We implement FaaSVA and show that FaaSVA can reduce video analytics end-to-end latency by 28.46%.

Perturbation-based mechanisms, such as differential privacy, mitigate gradient leakage attacks by introducing noise into the gradients, thereby preventing attackers from reconstructing clients' private data from the leaked gradients. However, can gradient perturbation protection mechanisms truly defend against all gradient leakage attacks? In this paper, we present the first attempt to break the shield of gradient perturbation protection in Federated Learning for the extraction of private information. We focus on common noise distributions, specifically Gaussian and Laplace, and apply our approach to DNN and CNN models. We introduce Mjölnir, a perturbation-resilient gradient leakage attack that is capable of removing perturbations from gradients without requiring additional access to the original model structure or external data. Specifically, we leverage the inherent diffusion properties of gradient perturbation protection to develop a novel diffusion-based gradient denoising model for Mjölnir. By constructing a surrogate client model that captures the structure of perturbed gradients, we obtain crucial gradient data for training the diffusion model. We further utilize the insight that monitoring disturbance levels during the reverse diffusion process can enhance gradient denoising capabilities, allowing Mjölnir to generate gradients that closely approximate the original, unperturbed versions through adaptive sampling steps. Extensive experiments demonstrate that Mjölnir effectively recovers the protected gradients and exposes the Federated Learning process to the threat of gradient leakage, achieving superior performance in gradient denoising and private data recovery.