Federated Learning (FL), an emerging decentralized Machine Learning (ML) approach, offers an effective avenue for training models on distributed data while safeguarding consumer privacy. Nevertheless, adversarial attacks initiated by malicious clients gaining access to confidential consumer data may deteriorate the learning process and performance. We develop the novel MADE-PI technique for the conventional FL that identifies and excludes malicious clients in the early model training phases. Our method incurs a novel local models’ preprocessing step performed before the aggregation procedure, in which we analyze the distribution of PI scores assigned to each client and exclude potentially poisoned data sources. We introduce Proportional – Integral (PI) scores as distance-based detection metrics inspired by PID control that consider the current distance between clients and the history of distances, through the proportional and integral terms respectively. Our empirical study on three distinct consumer application FL datasets, which represent use-cases from the intelligent transportation, handwriting recognition and medical scenarios, verifies that our approach is successful in identification and removal of compromised clients, mitigating threats to consumer model integrity and the application performance. The possible gain in learning effectiveness and efficiency against conventional methods is evaluated and analyzed.

In this research, we address a critical vulnerability of Federated Learning (FL) in practical applications: their susceptibility to adversarial attacks from malicious participants, which can severely degrade a global model’s performance. We propose MADE-PI, a novel defense mechanism designed to identify and exclude malicious clients early in the training process. Our method introduces a pre-aggregation step where the server evaluates each client update using a Proportional-Integral (PI) score. This score is a unique distance-based metric inspired by the PID controller, where a proportional term P measures the immediate deviation of a client’s update, and an integral term I tracks its behavior over time. By analyzing the distribution of these scores, our technique effectively flags and removes malicious contributions. We validate our approach through an empirical study on three datasets representing various applications of FL, including intelligent transportation, medical image analysis, and handwriting recognition. We concentrate on data poisoning as a highly relevant class of attacks. The results demonstrate that MADE-PI outperforms conventional defenses by providing superior malicious client detection and improving both learning efficiency and final model accuracy.

Machine Learning (ML) systems are integrated with other parts of modern cyberinfrastructure (CI), which forms the novel ML with Integrated Network (MLIN) structure. Various security tools are already employed in CI to detect malicious attacks. However, their operation is limited to a certain CI part or MLIN component without considering their integration and the ML end performance as the major indicator. We design and implement Knowledge Integration System Service (KISS) that introduces a novel approach to detect and classify attacks into adversarial attacks against ML end system (A-Attacks), and against base MLIN infrastructure (B-Attacks). Unlike traditional security mechanisms, KISS examines how attacks on individual MLIN components impact the overall ML end system performance, and extracts and integrates knowledge from each MLIN CI component to better distinguish between the attack types. As our major contributions, we (1) investigate the effects of A- and B-Attacks on ML systems, actively used in industry. We (2) develop the KISS prototype and verify it in practice. We demonstrate how KISS can be integrated into already established MLIN CI and combined with the traditional security tools. Our experiments verify that KISS improves attack detection and their initial classification between A- and B-Attacks in MLIN operational setups.

In this paper, we present an innovative approach to automate key event detection and highlights generation from soccer match videostreams that allows to improve accuracy and reliability, as well as to reduce data consumption and training time. Our method segments the videostream into distinct frames based on camera angles and activities, and integrates intelligent video analytics with additional visual information provided by broadcasters. As our major novelty in comparison to other intelligent soccer video analysis approaches, we deploy a Multi-Class Image Classifier to segment the video into wide-angle overviews, close-ups, and in-game replays, which allows us to improve the event detection performance and the quality of generated highlights. As another major novelty, we leverage YOLOv8 to detect events such as bookings, substitutions, and goals based on the additional information portrayed by broadcasters during the game. We evaluate our approach and demonstrate its advantage, when the additional information from broadcasters is available, against existing ones that analyze only the actions happening in the scene itself, such as the players' current positions and their actions between the frames. We evaluate our pipeline on a real soccer game recording and compare the highlights it generates with the official highlights provided by the broadcaster. Our pipeline demonstrates ample performance and is able to detect all key events in the game.