Chaoyang He

Central Server Free Federated Learning over Single-sided Trust Social Networks

2019 · arXiv (Cornell University)

Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated …

L-BGNN: Layerwise Trained Bipartite Graph Neural Networks

2022 · IEEE Transactions on Neural Networks and Learning Systems

Learning low-dimensional representations of bipartite graphs enables e-commerce applications, such as recommendation, classification, and link prediction. A layerwise-trained bipartite graph neural network (L-BGNN) embedding method, which is unsupervised, efficient, and scalable, is proposed in this …

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

2021 · arXiv (Cornell University)

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks. Federated learning (FL) provides promising approaches for a large number of clients …

Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

2023 · arXiv (Cornell University)

Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and …