… Federated learning (FL) faces challenges of intermittent … In this paper, we provide a unified
convergence analysis for FL … Then, we present a novel analysis that captures the effect of …

… To generate heterogeneous data that resembles a real federated learning setup, we will
follow a similar approach as in [43]. In this regard, we will distribute the data among clients in a …

… In this section, we will evaluate the proposed unified multimodal federated learning method
in multimodal activity recognition. We first describe the two activity recognition datasets used …

… a fair federated framework and a corresponding unified group … for unified group fairness,
we develop an efficient federated … fair federated learning methods on unified group fairness. …

… We apply our unified analysis for SoteriaFL and obtain theoretical guarantees for several
new private FL algorithms, including SoteriaFL-GD, SoteriaFL-SGD, SoteriaFL-SVRG, and …

… One way to overcome these challenges is federated learning (FL). FL allows distributed
data analysis by only exchanging model parameters and local models instead of sensitive raw …

… In this paper, we analyze the reason for the low model performance and unfair performance
distribution under the non-IID federated learning (FL) scenario, and propose a novel …

… machine learning technique called Federated Learning (FL), in which the model training is
distributed over mobile user equipment (UEs), exploiting UEs’ local computation and training …

… research directions of federated learning. Finally, we summarize the characteristics of
existing federated learning, and analyze the current practical application of federated learning. …

… robust, personalized federated learning, called Fed+, that unifies many federated learning …
accommodate the real-world characteristics found in federated training, such as the lack of IID …