Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

Natural language processing (NLP) systems have been proven to be vulnerable to backdoor
attacks, whereby hidden features (backdoors) are trained into a language model and may …

Data poisoning attacks and backdoor attacks aim to corrupt a machine learning classifier via
modifying, adding, and/or removing some carefully selected training examples, such that the …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

Contrastive learning pre-trains an image encoder using a large amount of unlabeled data
such that the image encoder can be used as a general-purpose feature extractor for various …

Local differential privacy is a widely studied restriction on distributed algorithms that collect
aggregates about sensitive user data, and is now deployed in several large systems. We …