We study federated contextual linear bandits, where M agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We consider the asynchronous setting, where all agents work independently and the communication between one agent and the server will not trigger other agents' communication. We propose a simple algorithm named FedLinUCBbased on the principle of optimism.

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the $K$-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization.

Most of existing federated learning (FL) formulation is treated as a point-estimate of models, inherently prone to overfitting on scarce client-side data with overconfident decisions. Though Bayesian inference can alleviate this issue, a direct posterior inference at clients may result in biased local posterior estimates due to data heterogeneity, leading to a sub-optimal global posterior. From an information-theoretic perspective, we propose FedMDMI, a federated posterior inference framework based on model-data mutual information (MI). Specifically, a global model-data MI term is introduced as regularization to enforce the global model to learn essential information from the heterogeneous local data, alleviating the bias caused by data heterogeneity and hence enhancing generalization. To make this global MI tractable, we decompose it into local MI terms at the clients, converting the global objective with MI regularization into several locally optimizable objectives based on local data. For these local objectives, we further show that the optimal local posterior is a Gibbs posterior, which can be efficiently sampled with stochastic gradient Langevin dynamics methods.

Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collaboratively learning a joint NE model is difficult. The key challenge lies in the objective function, as effective visualization algorithms like NE require computing loss functions among pairs of data. In this paper, we introduce \textsc{FedNE}, a novel approach that integrates the \textsc{FedAvg} framework with the contrastive NE technique, without any requirements of shareable data. To address the lack of inter-client repulsion which is crucial for the alignment in the global embedding space, we develop a surrogate loss function that each client learns and shares with each other. Additionally, we propose a data-mixing strategy to augment the local data, aiming to relax the problems of invisible neighbors and false neighbors constructed by the local $k$NN graphs. We conduct comprehensive experiments on both synthetic and real-world datasets. The results demonstrate that our \textsc{FedNE} can effectively preserve the neighborhood data structures and enhance the alignment in the global embedding space compared to several baseline methods.

In what follows, we will focus on this regime and assume that d > n.

Work done during an internship at Google Research 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

Then each client alternately estimates its cluster labels and refines the model estimates based on FedAvg or FedProx.

Table 5 shows the positioning of FedL2P against existing literature. This personalized policy can either 1) be fixed, e.g. FedEx which randomly samples per-client hyperparameters from learned categorical distributions. Scenarios where it's expensive to train from scratch for a new group of clients, e.g. This is illustrated in Section 4.4 where we adapt a publicly available pretrained Scenarios where it's important to also maintain a global model with high initial accuracy - Note that our approach also does not critically depend on the global model's performance.

Collaborations among multiple organizations, such as financial institutions, medical centers, and retail markets in decentralized settings are crucial to providing improved service and performance. However, the underlying organizations may have little interest in sharing their local data, models, and objective functions. These requirements have created new challenges for multi-organization collaboration. In this work, we propose Gradient Assisted Learning (GAL), a new method for multiple organizations to assist each other in supervised learning tasks without sharing local data, models, and objective functions. In this framework, all participants collaboratively optimize the aggregate of local loss functions, and each participant autonomously builds its own model by iteratively fitting the gradients of the overarching objective function. We also provide asymptotic convergence analysis and practical case studies of GAL. Experimental studies demonstrate that GAL can achieve performance close to centralized learning when all data, models, and objective functions are fully disclosed.