论文标题
在多任务增强学习中，个性化联邦超级核武器，用于隐私保存
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning
论文作者
论文摘要
当前，多机构增强学习专注于所有数据和培训都可以集中到一台机器的实现上。但是，如果本地代理商分为多个任务，并且需要在每个任务之间将数据私有化怎么办？我们开发了个性化联合超级核武器（PFH）在增强学习（RL）中的第一个应用。然后，我们提出了PFH在几个射击转移中的新颖应用，并显示了学习的显着初始增加。 PFH从未被证明超出监督学习基准，因此我们将PFH应用于一个重要领域：RL价格设定以进行能源需求响应。我们考虑了一个跨多个微电网分配给代理的一般情况，其中必须将能量消耗数据保存在每个微电网中。我们的工作共同探讨了个性化联合学习和RL的领域如何聚集在一起，以在确保数据安全的同时使学习效率有效。
Multi-Agent Reinforcement Learning currently focuses on implementations where all data and training can be centralized to one machine. But what if local agents are split across multiple tasks, and need to keep data private between each? We develop the first application of Personalized Federated Hypernetworks (PFH) to Reinforcement Learning (RL). We then present a novel application of PFH to few-shot transfer, and demonstrate significant initial increases in learning. PFH has never been demonstrated beyond supervised learning benchmarks, so we apply PFH to an important domain: RL price-setting for energy demand response. We consider a general case across where agents are split across multiple microgrids, wherein energy consumption data must be kept private within each microgrid. Together, our work explores how the fields of personalized federated learning and RL can come together to make learning efficient across multiple tasks while keeping data secure.
