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Help Advanced Search. Federated learning holds great promise in learning from fragmented sensitive data and has revolutionized how machine learning models are trained. This article provides a systematic overview and detailed taxonomy of federated learning. We investigate the existing security challenges in federated learning and provide a comprehensive overview of established defense techniques for data poisoning, inference attacks, and model poisoning attacks.
The work also presents an overview of current training challenges for federated learning, focusing on handling non-i. Finally, we discuss the remaining challenges in managing federated learning training and suggest focused research directions to address the open questions. Potential candidate areas for federated learning, including IoT ecosystem, healthcare applications, are discussed with a particular focus on banking and financial domains.
Architects and systems designers artfully balance multiple competing design constraints during the design process but are unable to translate between system metrics and end user experience. This work presents three methodologies to fill in this gap. However, while highly accurate the methodology is a painstaking process and does not scale with large numbers of participants. Finally, to allow for even greater scalability, we introduce a third methodology -- a survey -- and observe that the lack of incentive compatibility and the lack of hands-on experience with throttled device performance skews the results significantly, thus demonstrating the need for lab-based or incentive compatible study designs.
By quantifying the tradeoff between user satisfaction and performance, we enable architects and systems designers to make more nuanced tradeoffs between design requirements. One-stage algorithm have been widely used in target detection systems that need to be trained with massive data.
