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Found 5 papers, 2 papers with code
Probably Approximately Correct Federated Learning
However, existing multi-objective optimization frameworks are very time-consuming, and do not guarantee the existence of the Pareto frontier, this motivates us to seek a solution to transform the multi-objective problem into a single-objective problem because it is more efficient and easier to be solved.
Dynamic backdoor attacks against federated learning
In this paper, we focus on dynamic backdoor attacks under FL setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining a good performance on the main task, current existing studies are mainly focused on static backdoor attacks, that is the poison pattern injected is unchanged, however, FL is an online learning framework, and adversarial targets can be changed dynamically by attacker, traditional algorithms require learning a new targeted task from scratch, which could be computationally expensive and require a large number of adversarial training examples, to avoid this, we bridge meta-learning and backdoor attacks under FL setting, in which case we can learn a versatile model from previous experiences, and fast adapting to new adversarial tasks with a few of examples.
RPN: A Residual Pooling Network for Efficient Federated Learning
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
Real-World Image Datasets for Federated Learning
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.
