Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models

Federated Learning (FL) enables decentralized training of machine learning models on distributed data while preserving privacy. However, in real-world FL settings, client data is often non-identically distributed and imbalanced, resulting in statistical data heterogeneity which impacts the generalization capabilities of the server's model across clients, slows convergence and reduces performance. In this paper, we address this challenge by first proposing a characterization of statistical data heterogeneity by means of 6 metrics of global and client attribute imbalance, class imbalance, and spurious correlations. Next, we create and share 7 computer vision datasets for binary and multiclass image classification tasks in Federated Learning that cover a broad range of statistical data heterogeneity and hence simulate real-world situations. Finally, we propose FedDiverse, a novel client selection algorithm in FL which is designed to manage and leverage data heterogeneity across clients by promoting collaboration between clients with complementary data distributions. Experiments on the seven proposed FL datasets demonstrate FedDiverse's effectiveness in enhancing the performance and robustness of a variety of FL methods while having low communication and computational overhead.

Addressing Membership Inference Attack in Federated Learning with Model Compression

Federated Learning (FL) has been proposed as a privacy-preserving solution for machine learning. However, recent works have shown that Federated Learning can leak private client data through membership attacks. In this paper, we show that the effectiveness of these attacks on the clients negatively correlates with the size of the client datasets and model complexity. Based on this finding, we propose model-agnostic Federated Learning as a privacy-enhancing solution because it enables the use of models of varying complexity in the clients. To this end, we present $\texttt{MaPP-FL}$, a novel privacy-aware FL approach that leverages model compression on the clients while keeping a full model on the server. We compare the performance of $\texttt{MaPP-FL}$ against state-of-the-art model-agnostic FL methods on the CIFAR-10, CIFAR-100, and FEMNIST vision datasets. Our experiments show the effectiveness of $\texttt{MaPP-FL}$ in preserving the clients' and the server's privacy while achieving competitive classification accuracies.

A Snapshot of the Frontiers of Client Selection in Federated Learning

Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na\"{i}ve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.

Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks

This paper proposes an interactive system for mobile devices controlled by hand gestures aimed at helping people with visual impairments. This system allows the user to interact with the device by making simple static and dynamic hand gestures. Each gesture triggers a different action in the system, such as object recognition, scene description or image scaling (e.g., pointing a finger at an object will show a description of it). The system is based on a multi-head neural network architecture, which initially detects and classifies the gestures, and subsequently, depending on the gesture detected, performs a second stage that carries out the corresponding action. This multi-head architecture optimizes the resources required to perform different tasks simultaneously, and takes advantage of the information obtained from an initial backbone to perform different processes in a second stage. To train and evaluate the system, a dataset with about 40k images was manually compiled and labeled including different types of hand gestures, backgrounds (indoors and outdoors), lighting conditions, etc. This dataset contains synthetic gestures (whose objective is to pre-train the system in order to improve the results) and real images captured using different mobile phones. The results obtained and the comparison made with the state of the art show competitive results as regards the different actions performed by the system, such as the accuracy of classification and localization of gestures, or the generation of descriptions for objects and scenes.