Edge-assisted Democratized Learning Towards Federated Analytics

TL;DR

This paper introduces Edge-DemLearn, a hierarchical edge-assisted democratized learning framework that enhances federated analytics by improving model generalization and scalability through distributed control, optimized resource allocation, and real-world dataset validation.

Contribution

The paper proposes a novel hierarchical edge-assisted democratized learning mechanism, Edge-DemLearn, that improves federated analytics by enhancing model generalization and scalability using distributed control and optimized resource matching.

Findings

Edge-DemLearn improves model generalization in federated analytics.

The framework reduces communication loads and enhances scalability.

Simulation results validate the effectiveness on real datasets.

Abstract

A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often results in learning models with poor generalization, i.e., an ability to handle new/unseen data, for real-world applications. Moreover, a hierarchical FL structure with distributed computing platforms demonstrates incoherent model performances at different aggregation levels. Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability. In this work, we adopt the novel democratized learning (Dem-AI) principles and designs to meet these objectives. Firstly, we show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn, as a practical framework to empower generalization capability in support of FA. Secondly, we validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions by leveraging the distributed computing infrastructure. The distributed edge computing servers construct regional models, minimize the communication loads, and ensure distributed data analytic application's scalability. To that end, we adhere to a near-optimal two-sided many-to-one matching approach to handle the combinatorial constraints in Edge-DemLearn and solve it for fast knowledge acquisition with optimization of resource allocation and associations between multiple servers and devices. Extensive simulation results on real datasets demonstrate the effectiveness of the proposed methods.