In this work, a novel federated distillation weight aggregation method is proposed. Specifically, an algorithm designed for effective learning in distributed environments is introduced. This algorithm includes an innovative federated distillation scheme, proposing a sophisticated aggregation of model outputs, employing a global server model to manage process. On the server side, feature mixing is employed to aggregate client representations before they are transmitted back to the client side for knowledge distillation. During feature mixing a weight factor is assigned to each client’s logits, penalizing bad quality clients and preserving system’s credibility. Thorough experimentation has been conducted to comprehensively study the issue at hand. Key findings reveal the significant potential of the proposed solution, which achieves robust performance in a federated setting while reducing communication costs.

In this work, a novel federated distillation weight aggregation method is proposed. Specifically, an algorithm designed for effective learning in distributed environments is introduced. This algorithm includes an innovative federated distillation scheme, proposing a sophisticated aggregation of model outputs, employing a global server model to manage process. On the server side, feature mixing is employed to aggregate client representations before they are transmitted back to the client side for knowledge distillation. During feature mixing a weight factor is assigned to each client’s logits, penalizing bad quality clients and preserving system’s credibility. Thorough experimentation has been conducted to comprehensively study the issue at hand. Key findings reveal the significant potential of the proposed solution, which achieves robust performance in a federated setting while reducing communication costs.

This paper addresses the challenge of reducing annotation costs in distributed learning environments, particularly in systems with limited data and computational resources, such as those found in edge devices. We propose Federated Reinforced Active Learning, a framework that integrates Federated Learning with Reinforced Active Learning to optimize data labeling under strict cost constraints. The method is designed for small-scale networks where data is sparse, and minimal training epochs are available. By utilizing reinforcement learning within active learning, the system selects the most informative data samples, allowing for efficient training while significantly reducing the need for extensive annotations. This approach is particularly suited for environments where minimizing both annotation and computational costs is critical, such as in applications where cost efficiency and resource limitations are top priorities. The proposed method is evaluated on the CIFAR-10 and CIFAR-100 datasets using ResNet18, across 5 and 10 clients. Results demonstrate that the method significantly reduces annotation costs and improves learning outcomes, making it an ideal solution for cost-sensitive distributed systems.

It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter’s core objectives are to monitor criminal and terrorist related activities and to identify the ”key players” in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today’s social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.

Driven by the needs of customers and industry, online fashion search and analytics are recently gaining much attention. As fashion is mostly expressed by visual content, the analysis of fashion images in online social networks is a rich source of possible insights on evolving trends and customer preferences. Although a plethora of visual content is available, the modeling of clothes’ physics and movement, the implicit semantics in fashion designs, and the subjectivity of their interpretation pose difficulties to fully automated solutions for fashion search and analysis. In this article, we present the design and evaluation of a crowd-powered system for fashion similarity search from Twitter, supporting trend analysis for fashion professionals. The system enables fashion similarity search based on specific human-based similarity criteria. This is achieved by implementing a novel machine--crowd workflow that supports complex tasks requiring highly subjective judgments where multiple true solutions may coexist. We discuss how this leads to a novel class of crowd-powered systems for which the output of the crowd is not used to verify the automatic analysis but is the desired outcome. Finally, we show how this kind of crowd involvement enables a novel kind of similarity search and represents a crucial factor for the acceptance of system results by the end user.

In this paper, a novel framework for 3D object retrieval is presented. The paper focuses on the investigation of an accurate 3D model alignment method, which is achieved by combining two intuitive criteria, the plane reflection symmetry and rectilinearity. After proper positioning in a coordinate system, a set of 2D images (multi-views) are automatically generated from the 3D object, by taking views from uniformly distributed viewpoints. For each image, a set of flip-invariant shape descriptors is extracted. Taking advantage of both the pose estimation of the 3D objects and the flip-invariance property of the extracted descriptors, a new matching scheme for fast computation of 3D object dissimilarity is introduced. Experiments conducted in SHREC 2009 benchmark show the superiority of the pose estimation method over similar approaches, as well as the efficiency of the new matching scheme.