Accelerating Fair Federated Learning: Adaptive Federated Adam

Federated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data of different parties. However, when datasets of participants are not independent and identically distributed (non-IID), models trained by naive federated algorithms may be biased towards certain participants, and model performance across participants is non-uniform. This is known as the fairness problem in federated learning. In this paper, we formulate fairness-controlled federated learning as a dynamical multi-objective optimization problem to ensure fair performance across all participants. To solve the problem efficiently, we study the convergence and bias of Adam as the server optimizer in federated learning, and propose Adaptive Federated Adam (AdaFedAdam) to accelerate fair federated learning with alleviated bias. We validated the effectiveness, Pareto optimality and robustness of AdaFedAdam in numerical experiments and show that AdaFedAdam outperforms existing algorithms, providing better convergence and fairness properties of the federated scheme

Handling Group Fairness in Federated Learning Using Augmented Lagrangian Approach

Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models might be biased towards sensitive factors such as race or gender, even if they are trained using a legally compliant process. To redress this concern, this paper proposes a novel FL algorithm designed explicitly to address group fairness issues. We show empirically on CelebA and ImSitu datasets that the proposed method can improve fairness both quantitatively and qualitatively with minimal loss in accuracy in the presence of statistical heterogeneity and with different numbers of clients. Besides improving fairness, the proposed FL algorithm is compatible with local differential privacy (LDP), has negligible communication costs, and results in minimal overhead when migrating existing FL systems from the common FL protocol such as FederatedAveraging (FedAvg). We also provide the theoretical convergence rate guarantee for the proposed algorithm and the required noise level of the Gaussian mechanism to achieve desired LDP. This innovative approach holds significant potential to enhance the fairness and effectiveness of FL systems, particularly in sensitive applications such as healthcare or criminal justice.Comment: the main paper has 8 pages and the supplementary material has 12 pages. At the time of uploading, it is currently under review in ECA

Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning. DSVGD maintains a number of non-random and interacting particles at a central server to represent the current iterate of the model global posterior. The particles are iteratively downloaded and updated by one of the agents with the end goal of minimizing the global free energy. By varying the number of particles, DSVGD enables a flexible trade-off between per-iteration communication load and number of communication rounds. DSVGD is shown to compare favorably to benchmark frequentist and Bayesian federated learning strategies, also scheduling a single device per iteration, in terms of accuracy and scalability with respect to the number of agents, while also providing well-calibrated, and hence trustworthy, predictions

Exploiting Shared Representations for Personalized Federated Learning

Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. However, the fruits of representation learning have yet to be fully-realized in federated settings. Although data in federated settings is often non-i.i.d. across clients, the success of centralized deep learning suggests that data often shares a global feature representation, while the statistical heterogeneity across clients or tasks is concentrated in the labels. Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. We prove that this method obtains linear convergence to the ground-truth representation with near-optimal sample complexity in a linear setting, demonstrating that it can efficiently reduce the problem dimension for each client. Further, we provide extensive experimental results demonstrating the improvement of our method over alternative personalized federated learning approaches in heterogeneous settings