Decentralized Federated Distillation (DFD) has emerged as a significant research direction since it not only supports heterogeneous model training but also naturally avoids privacy risks and communication bottlenecks stemming from the central server. DFD shows great potential in various training scenarios, especially in cross-silo federated environments. Existing DFD algorithms have limitations, including reliance on public datasets for distillation, insufficient privacy protection, and high communication overhead. This paper introduces Ring-Distill, a novel DFD algorithm designed specifically for cross-silo federated environments, effectively addressing the aforementioned limitations. Ring-Distill allows clients to complete federated training within a few global epochs (i.e., communication rounds) without sharing public datasets, e.g.,$N$global epochs for a system involving$N$clients. To protect privacy and further reduce communication overhead, Ring-Distill contains a privacy-oriented automatic model pruning mechanism (PAMP) which can automatically create the privacy-preserving compressed model called proxy model for each client. These proxy models are employed for client-to-client distillation during each global epoch. Furthermore, Ring-Distill contains a historical model-based distillation (HMD) mechanism that allows local models to transfer knowledge from multiple historical proxy model replicas stored locally. Due to historical proxy model replicas, the HMD mechanism not only improves the distillation performance but also effectively mitigates client dropout issues. Theoretical analysis and comprehensive experiments show that Ring-Distill has significant advantages in terms of accuracy, privacy, and communication cost.

GPU clusters have been widely used to co-locate various deep learning (DL) workloads in a multi-tenant way. Although such resource sharing can significantly reduce training cost, resource contention and interference among co-located workloads make task scheduling very complex and challenging. To simplify the scheduling problem, existing algorithms usually divide the procedure of scheduling into two sub-tasks, i.e., task placement and resource allocation, and allocate resources according to pre-defined and fixed resource demands. However, such a paradigm significantly constrains the selection of potential scheduling solutions. In this article, we present MAIFS, a novel multi-agent reinforcement learning based scheduling algorithm that handles task placement and resource allocation integratedly, and allows fungible resource allocation based on resource sensitivity of DL workloads. The core of MAIFS lies in two mechanisms. The multi-agent attention mechanism is designed to learn and share inter-related resource state features observed from different agents, which enables agents to explore fungible resource allocation solutions. The dynamic coordination graph mechanism is designed for coordinating interactive task placement decisions of agents during integrated scheduling, so as to mitigate potential task conflicts. Simulated experiments using two large scale production DL workload traces and physical deployment experiments based on a Kubernetes based GPU cluster show that MAIFS can outperform state-of-the-art scheduling algorithms by up to 44% in terms of makespan and 46% in terms of job completion time (JCT).

Recently, sparsity based classification has been applied to video anomaly detection. A linear model is assumed over video features (e.g. trajectories) such that the feature representation of a new event is written as a sparse linear combination of existing feature representations in the dictionary. Sparsity based video anomaly detection shows promise but open challenges remain in that existing methods assume object specific and class specific event dictionaries making them applicable mostly in highly structured scenarios. Second, using conventional sparsity models on matrices/vectors, the computational burden is often high. In this work, we advocate a more general and practical sparsity model using a low-rank structure on the matrix of sparse coefficients. We find that enforcing a low-rank structure can ease the rigidity of traditional row-sparse constraints on sparse coefficient vectors/matrices. Because low-rank matrices are of course not always sparse, an additional l1regularization term is added. Further, if rank is substituted by its convex nuclear norm alternative, then significant computational benefits can be obtained over existing methods in sparsity based video anomaly detection. Experimental evaluation on benchmark video datasets reveal, our method is competitive with state-of-the art while providing robustness benefits under occlusion.

Recently, sparsity based classification has been applied to video anomaly detection. A linear model is assumed over video features (e.g. trajectories) such that the feature representation of a new event is written as a sparse linear combination of existing feature representations in the dictionary. Sparsity based video anomaly detection has shown promise over alternate video anomaly detection methods in that the sparse representations exhibit excellent robustness under noise (common in surveillance videos) and missing or corrupted features, e.g. vehicle occlusion in transportation videos. One limitation of existing sparsity based video anomaly detection techniques is that they are based on only a single feature representation (known formally as video event encoding). One can easily envision that different event representations such as object trajectories and spatio-temporal volumes often contain correlated yet complementary information. In this paper, we propose to extend sparsity models based on single feature representations to simultaneous sparse representations of multiple feature representations. In this model, the matrix of sparse coefficients does not confirm to the commonly seen row-sparsity and a modified greedy heuristic approach that extends simultaneous orthogonal matching pursuit (SOMP) is needed to solve the resulting optimization problem. Experiments on two benchmark video datasets reveal that our method significantly outperforms state-of-the art approaches that utilize only a single-perspective or event encoding.

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies on object tracking and trajectory analysis. Very recently, sparse reconstruction techniques have been employed in video anomaly detection. The fundamental underlying assumption of these methods is that any new feature representation of a normal/anomalous event can be approximately modeled as a (sparse) linear combination prelabeled feature representations (of previously observed events) in a training dictionary. Sparsity can be a powerful prior on model coefficients but challenges remain in the detection of anomalies involving multiple objects and the ability of the linear sparsity model to effectively allow for class separation. The proposed research addresses both these issues. First, we develop a new joint sparsity model for anomaly detection that enables the detection of joint anomalies involving multiple objects. This extension is highly nontrivial since it leads to a new simultaneous sparsity problem that we solve using a greedy pursuit technique. Second, we introduce nonlinearity into, that is, kernelize. The linear sparsity model to enable superior class separability and hence anomaly detection. We extensively test on several real world video datasets involving both single and multiple object anomalies. Results show marked improvements in detection of anomalies in both supervised and unsupervised scenarios when using the proposed sparsity models.

An important computational problem in color imaging is the design of color transforms that map color between devices or from a device-dependent space (e.g., RGB/CMYK) to a device-independent space (e.g., CIELAB) and vice versa. Real-time processing constraints entail that such nonlinear color transforms be implemented using multidimensional lookup tables (LUTs). Furthermore, relatively sparse LUTs (with efficient interpolation) are employed in practice because of storage and memory constraints. This paper presents a principled design methodology rooted in constrained convex optimization to design color LUTs on a simplex topology. The use of n simplexes, i.e., simplexes in n dimensions, as opposed to traditional lattices, recently has been of great interest in color LUT design for simplex topologies that allow both more analytically tractable formulations and greater efficiency in the LUT. In this framework of n-simplex interpolation, our central contribution is to develop an elegant iterative algorithm that jointly optimizes the placement of nodes of the color LUT and the output values at those nodes to minimize interpolation error in an expected sense. This is in contrast to existing work, which exclusively designs either node locations or the output values. We also develop new analytical results for the problem of node location optimization, which reduces to constrained optimization of a large but sparse interpolation matrix in our framework. We evaluate our n -simplex color LUTs against the state-of-the-art lattice (e.g., International Color Consortium profiles) and simplex-based techniques for approximating two representative multidimensional color transforms that characterize a CMYK xerographic printer and an RGB scanner, respectively. The results show that color LUTs designed on simplexes offer very significant benefits over traditional lattice-based alternatives in improving color transform accuracy even with a much smaller number of nodes.