阅读笔记 | Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning

Summary

The paper argue that the efficiency and data privacy of Federated Learning are non-orthogonal from the perspective of model training, which means they are restricting each other. So that the paper strictly formulates the problem at first, and designs a cloud-edge-end hierarchical FL system with adaptive control algorithm embedding a two-level Differential Protection method to relieve both the resource and privacy concerns. The design follows the following ideas:

杂记 | greedy与soft-greedy策略

44.358730,11.593990; Generated by AI44.358730,11.593990; Generated by AI

生活中,许多人似乎都在解决着自己的多目标优化问题。我们即想要这个,又想要那个,有时候目标间甚至相互矛盾,你存我亡。

但其实我们没有严谨的公式和高速计算的能力,去把一个个问题形式化,然后一一求解。

而贪心策略(Greedy Strategy),也就是选择当前最好的,作为一种符合直觉的方式,被广泛地应用到我们生活中,而且很多时候其实我们并不会意识到。