The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

In federated learning (FL), weighted aggregation of local models is conducted to generate a
global model, and the aggregation weights are normalized (the sum of weights is 1) and …

Federated learning (FL) is a distributed machine learning technique that enables
collaborative model training while avoiding explicit data sharing. The inherent privacy …

Federated learning learns from scattered data by fusing collaborative models from local
nodes. However, conventional coordinate-based model averaging by FedAvg ignored the …

With the development of AI technology, there is an increasing awareness and concern about
data privacy, and training data is becoming more and more fragmented. To make better use …

We address the relatively unexplored problem of hyper-parameter optimization (HPO) for
federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA) …

We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-
HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO …

The generation of large amounts of personal data provides data centers with sufficient
resources to mine idiosyncrasy from private records. User modeling has long been a …

Federated learning enables multiple clients to learn a general model without sharing local
data, and the federated learning system also improves information security and advances …

Performing hyperparameter tuning in federated learning is often prohibitively expensive due
to the substantial communication overhead associated with training a single configuration …