… Federated learning (FL) presents an innovative framework facilitating collaborative ML
model training among multiple entities without requiring direct access to their respective local …

… We propose an asynchronous aggregation scheme for federated learning. We compare
its performance to the baseline synchronous federated-averaging algorithm [7]. We show its …

… 2.4 Problem Formulation We define the adaptive asynchronous federated learning with
resource constraints (AAFL-RC) problem as below. Given a federated learning task, we will …

… develop an asynchronous online federated learning framework with … We propose an
asynchronous online federated learning … from lagging clients, and clients perform online learning …

… models in federated learning by incorporating local differential privacy (LDP) into
gradient descent training scheme. 2) We develop a new asynchronous federated learning architecture …

… Federated Learning (FL), a novel decentralized learning paradigm that can unify massive …
However, the performance dropping of FL running in a heterogeneous and asynchronous …

… We first describe our vertical asynchronous federated learning (VAFL) algorithm in a high
level as follows. During the learning process, from the server side, it waits until receiving a …

… asynchronous FL framework, which well adapts to the heterogeneity of users, communication
environments and learning … validate that the proposed asynchronous FL framework can …

… asynchronous training algorithms for VP data while keeping the data privacy. In this article,
we propose an asynchronous federated … the first asynchronous federated learning algorithms …

… asynchronous federated learning (BAFL) architecture is proposed that combines the blockchain
with asynchronous FL, with the advantages of trust and learning … an asynchronous FL …