# Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent

arXiv: https://arxiv.org/abs/2112.09341 · HTML: https://mlsanity.com/paper/2112.09341/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [Decentralized Collaborative Learning of Personalized Models over Networks](https://mlsanity.com/paper/1610.05202/) (2016)
- [Training Neural Networks Without Gradients: A Scalable ADMM Approach](https://mlsanity.com/paper/1605.02026/) (2016)
- [Personalized and Private Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1705.08435/) (2017)
- [Optimal Algorithms for Non-Smooth Distributed Optimization in Networks](https://mlsanity.com/paper/1806.00291/) (2018)
- [Graph Embedding for Recommendation against Attribute Inference Attacks](https://mlsanity.com/paper/2101.12549/) (2021)
- [Global Convergence of Block Coordinate Descent in Deep Learning](https://mlsanity.com/paper/1803.00225/) (2018)
- [Try This Instead: Personalized and Interpretable Substitute Recommendation](https://mlsanity.com/paper/2005.09344/) (2020)
- [Fast-adapting and Privacy-preserving Federated Recommender System](https://mlsanity.com/paper/2104.00919/) (2021)
- [A globally convergent algorithm for nonconvex optimization based on block coordinate update](https://mlsanity.com/paper/1410.1386/) (2014)
- [A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks](https://mlsanity.com/paper/1809.00710/) (2018)
- [Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks](https://mlsanity.com/paper/1711.07354/) (2017)
- [IPLS : A Framework for Decentralized Federated Learning](https://mlsanity.com/paper/2101.01901/) (2021)
- [Lifted Neural Networks](https://mlsanity.com/paper/1805.01532/) (2018)
- [Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training](https://mlsanity.com/paper/1811.08039/) (2018)
- [Learning Elastic Embeddings for Customizing On-Device Recommenders](https://mlsanity.com/paper/2106.02223/) (2021)
- [A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training](https://mlsanity.com/paper/1803.09082/) (2018)
- [Distributed Learning over Unreliable Networks](https://mlsanity.com/paper/1810.07766/) (2018)
- [FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection](https://mlsanity.com/paper/2101.02873/) (2021)
- [Mathematical models for sleep-wake dynamics: comparison of the two-process model and a mutual inhibition neuronal model](https://mlsanity.com/paper/1311.1734/) (2013)

## Built on by (most cited first, 2 of 2)

- [Decentralized Collaborative Learning Framework for Next POI Recommendation](https://mlsanity.com/paper/2204.06516/) (2022)
- [Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation](https://mlsanity.com/paper/2308.13269/) (2023)

## Abstract (arXiv)

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health analytics. Nevertheless, in the classic cloud-based/centralized e-health paradigms, all the data will be centrally stored on the server to facilitate model training, which inevitably incurs privacy concerns and high time delay. Distributed solutions like Decentralized Stochastic Gradient Descent (D-SGD) are proposed to provide safe and timely diagnostic results based on personal devices. However, methods like D-SGD are subject to the gradient vanishing issue and usually proceed slowly at the early training stage, thereby impeding the effectiveness and efficiency of training. In addition, existing methods are prone to learning models that are biased towards users with dense data, compromising the fairness when providing E-health analytics for minority groups. In this paper, we propose a Decentralized Block Coordinate Descent (D-BCD) learning framework that can better optimize deep neural network-based models distributed on decentralized devices for E-health analytics. Benchmarking experiments on three real-world datasets illustrate the effectiveness and practicality of our proposed D-BCD, where additional simulation study showcases the strong applicability of D-BCD in real-life E-health scenarios.