Federated learning (FL) is an effective machine learning paradigm where multiple clients
can train models based on heterogeneous data in a decentralized manner without …

With the development of modern internet techniques, Cross-Domain Recommendation
(CDR) systems have been widely exploited for tackling the data-sparsity problem …

Small on-device models have been successfully trained with user-level differential privacy
(DP) for next word prediction and image classification tasks in the past. However, existing …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …

To enhance the performance of large language models (LLM) on downstream tasks, one
solution is to fine-tune certain LLM parameters and make it better align with the …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Federated learning (FL) is a promising approach for enhancing data privacy preservation,
particularly for authentication systems. However, limited round communications, scarce …

Federated learning (FL) collaboratively models user data in a decentralized way. However,
in the real world, non-identical and independent data distributions (non-IID) among clients …

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of
benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and …