In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions. In our setting, clients’ distributions represent particular domains, and Fed-DaDiL collectively trains a federated dictionary of empirical distributions. In particular, we build upon the Dataset Dictionary Learning framework by designing collaborative communication protocols and aggregation operations. The chosen protocols keep clients’ data private, thus enhancing overall privacy compared to its centralized counterpart. We empirically demonstrate that our approach successfully generates labeled data on the target domain with extensive experiments on (i) Caltech-Office, (ii) TEP, and (iii) CWRU benchmarks. Furthermore, we compare our method to its centralized counterpart and other benchmarks in federated domain adaptation.

Federated Learning (FL) has gained prominence as a decentralized and privacy-preserving paradigm that enables multiple clients to collaboratively train a machine learning model under the supervision of a central server. Instead of centralizing the data, clients keep their data locally and share only model parameters during multiple communication rounds. However, recent attacks, such as gradient reconstruction attacks (GRAs) show privacy issues when an attacker knows the communication of a client. In the literature, these privacy issues are mainly explored when clients compute new parameters using a single gradient descent step on their data (FedSGD) and then send them back to the remote server. In a more realistic scenario, the clients' protocol is based on several gradient descent steps (FedAvg). This protocol adds intermediate computation steps, which are unknown from the attacker, thus making GRAs less successful. In this incremental paper, we conduct exhaustive experiments on four state-of-the-art attacks under the FedAvg protocol, on a very basic and a more complex neural network (ResNet-18) with CIFAR100 dataset. These experiments provide the following results 1) a privacy-utility trade-off analysis, 2) insights on the choice of attacks' hyperparameters, 3) the client's local learning rate has little impact on attacks' effectiveness 4) a proof that the privacy risk is not necessarily decreasing over rounds, contrary to common belief.

Robust and accurate path tracking for autonomous vehicle navigation is a complex task, especially when it comes to managing system uncertainties such as inertia, slippage, and action delays. Although model-based controllers are efficient, their performance can be limited by such uncertainties and by the complexity of the gain tuning process. To address this, our study evaluates the effectiveness of four strategies using reinforcement learning (RL) with a controller, to provide either - steering correction, full gain tuning, gain correction, or end-to-end learning without any controller - to improve trajectory tracking. These methods are trained on geometric controllers (Pure Pursuit, Stanley) and model predictive controllers (Romea, EBSF). Our results show that all RL methods improve tracking at high speeds, with steering correction proving the most consistently effective in all cases.

Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art.

Federated learning enables different parties to collaboratively build a global model under the orchestration of a server while keeping the training data on clients' devices. However, performance is affected when clients have heterogeneous data. To cope with this problem, we assume that despite data heterogeneity, there are groups of clients who have similar data distributions that can be clustered. In previous approaches, in order to cluster clients the server requires clients to send their parameters simultaneously. However, this can be problematic in a context where there is a significant number of participants that may have limited availability. To prevent such a bottleneck, we propose FLIC (Federated Learning with Incremental Clustering), in which the server exploits the updates sent by clients during federated training instead of asking them to send their parameters simultaneously. Hence no additional communications between the server and the clients are necessary other than what classical federated learning requires. We empirically demonstrate for various non-IID cases that our approach successfully splits clients into groups following the same data distributions. We also identify the limitations of FLIC by studying its capability to partition clients at the early stages of the federated learning process efficiently. We further address attacks on models as a form of data heterogeneity and empirically show that FLIC is a robust defense against poisoning attacks even when the proportion of malicious clients is higher than 50%.

In this paper, a novel gas identification approach based on the Recursive Least Squares (RLS) algorithm is proposed. We detail some adaptations of RLS to be applied to a sensor matrix of several technologies in optimal conditions. The low complexity of the algorithm and its ability to process online samples from multi-sensor make the real-time identification of volatile compounds possible. The effectiveness of this approach to early detect and recognize explosive compounds in the air has been successfully demonstrated on an experimentally obtained dataset.

The BioSecure Network of Excellence has collected a large multi- biometric publicly available database and organized the BioSecure Multimodal Evaluation Campaigns (BMEC) in 20072. This paper reports on the Talking Faces campaign. Open source reference systems were made available to participants and four laboratories submitted executable code to the organizer who performed tests on sequestered data. Several deliberate impostures were tested. It is demonstrated that forgeries are a real threat for such systems. A technological race is ongoing between deliberate impostors and system developers.