ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems

The integration of Federated Learning (FL) and Multi-Task Learning (MTL) has been explored to address client heterogeneity, with Federated Multi-Task Learning (FMTL) treating each client as a distinct task. However, most existing research focuses on data heterogeneity (e.g., addressing non-IID data) rather than task heterogeneity, where clients solve fundamentally different tasks. Additionally, much of the work relies on centralized settings with a server managing the federation, leaving the more challenging domain of decentralized FMTL largely unexplored. Thus, this work bridges this gap by proposing ColNet, a framework designed for heterogeneous tasks in decentralized federated environments. ColNet divides models into the backbone and task-specific layers, forming groups of similar clients, with group leaders performing conflict-averse cross-group aggregation. A pool of experiments with different federations demonstrated ColNet outperforms the compared aggregation schemes in decentralized settings with label and task heterogeneity scenarios.

From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning

Federated Learning (FL) is widely recognized as a privacy-preserving machine learning paradigm due to its model-sharing mechanism that avoids direct data exchange. However, model training inevitably leaves exploitable traces that can be used to infer sensitive information. In Decentralized FL (DFL), the overlay topology significantly influences its models' convergence, robustness, and security. This study explores the feasibility of inferring the overlay topology of DFL systems based solely on model behavior, introducing a novel Topology Inference Attack. A taxonomy of topology inference attacks is proposed, categorizing them by the attacker's capabilities and knowledge. Practical attack strategies are developed for different scenarios, and quantitative experiments are conducted to identify key factors influencing the attack effectiveness. Experimental results demonstrate that analyzing only the public models of individual nodes can accurately infer the DFL topology, underscoring the risk of sensitive information leakage in DFL systems. This finding offers valuable insights for improving privacy preservation in decentralized learning environments.

De-VertiFL: A Solution for Decentralized Vertical Federated Learning

Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been extensively studied in both centralized and decentralized settings. In contrast, Vertical Federated Learning (VFL), which is crucial in real-world decentralized scenarios where clients possess different, yet sensitive, data about the same entity, remains underexplored. Thus, this work introduces De-VertiFL, a novel solution for training models in a decentralized VFL setting. De-VertiFL contributes by introducing a new network architecture distribution, an innovative knowledge exchange scheme, and a distributed federated training process. Specifically, De-VertiFL enables the sharing of hidden layer outputs among federation clients, allowing participants to benefit from intermediate computations, thereby improving learning efficiency. De-VertiFL has been evaluated using a variety of well-known datasets, including both image and tabular data, across binary and multiclass classification tasks. The results demonstrate that De-VertiFL generally surpasses state-of-the-art methods in F1-score performance, while maintaining a decentralized and privacy-preserving framework.

Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

The rapid integration of Federated Learning (FL) into networking encompasses various aspects such as network management, quality of service, and cybersecurity while preserving data privacy. In this context, Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, negatively impacting its performance. Existing defense mechanisms have been designed for centralized FL and they do not adequately exploit the particularities of DFL. Thus, this work introduces Sentinel, a defense strategy to counteract poisoning attacks in DFL. Sentinel leverages the accessibility of local data and defines a three-step aggregation protocol consisting of similarity filtering, bootstrap validation, and normalization to safeguard against malicious model updates. Sentinel has been evaluated with diverse datasets and various poisoning attack types and threat levels, improving the state-of-the-art performance against both untargeted and targeted poisoning attacks.

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

The expansion of the Internet-of-Things (IoT) paradigm is inevitable, but vulnerabilities of IoT devices to malware incidents have become an increasing concern. Recent research has shown that the integration of Reinforcement Learning with Moving Target Defense (MTD) mechanisms can enhance cybersecurity in IoT devices. Nevertheless, the numerous new malware attacks and the time that agents take to learn and select effective MTD techniques make this approach impractical for real-world IoT scenarios. To tackle this issue, this work presents CyberForce, a framework that employs Federated Reinforcement Learning (FRL) to collectively and privately determine suitable MTD techniques for mitigating diverse zero-day attacks. CyberForce integrates device fingerprinting and anomaly detection to reward or penalize MTD mechanisms chosen by an FRL-based agent. The framework has been evaluated in a federation consisting of ten devices of a real IoT platform. A pool of experiments with six malware samples affecting the devices has demonstrated that CyberForce can precisely learn optimum MTD mitigation strategies. When all clients are affected by all attacks, the FRL agent exhibits high accuracy and reduced training time when compared to a centralized RL agent. In cases where different clients experience distinct attacks, the CyberForce clients gain benefits through the transfer of knowledge from other clients and similar attack behavior. Additionally, CyberForce showcases notable robustness against data poisoning attacks.