Federated Learning systems use a centralized server to aggregate model updates. This is a bandwidth and resource-heavy constraint and exposes the system to privacy concerns. We instead implement a peer to peer learning system in which nodes train on their own data and periodically perform a weighted average of their parameters with that of their peers according to a learned trust matrix. So far, we have created a model client framework and have been using this to run experiments on the proposed system using multiple virtual nodes which in reality exist on the same computer. We used this strategy as stated in Iteration 1 of our proposal to prove the concept of peer to peer learning with shared parameters. We now hope to run more experiments and build a more deployable real world system for the same.

翻译：摘要：联邦学习系统使用集中式服务器聚合模型更新。这种设计带来了带宽和资源的沉重负担，并引发隐私问题。为此，我们实现了一种点对点学习系统，其中各节点基于自身数据训练，并根据学习到的信任矩阵定期与对等节点进行参数的加权平均。目前，我们已构建了一个模型客户端框架，并利用多个实际存在于同一计算机上的虚拟节点对所提系统进行实验。如我们提案的迭代1所述，该策略用于验证共享参数的点对点学习概念。下一步，我们计划开展更多实验，并构建更易于部署的真实世界系统。