# Distributed data analytics

arXiv: https://arxiv.org/abs/2203.14088 · HTML: https://mlsanity.com/paper/2203.14088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 124 references that are in the map.
- First posted on arXiv 26 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Compressing Neural Networks with the Hashing Trick](https://mlsanity.com/paper/1504.04788/) (2015)
- [A Complete Recipe for Stochastic Gradient MCMC](https://mlsanity.com/paper/1506.04696/) (2015)
- [Consistency and fluctuations for stochastic gradient Langevin dynamics](https://mlsanity.com/paper/1409.0578/) (2014)
- [DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments](https://mlsanity.com/paper/2004.05703/) (2020)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [Asymptotically Exact, Embarrassingly Parallel MCMC](https://mlsanity.com/paper/1311.4780/) (2013)
- [A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics](https://mlsanity.com/paper/1703.02952/) (2017)
- [Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server](https://mlsanity.com/paper/1512.09327/) (2015)
- [Strategies and Principles of Distributed Machine Learning on Big Data](https://mlsanity.com/paper/1512.09295/) (2015)
- [Learning Privately from Multiparty Data](https://mlsanity.com/paper/1602.03552/) (2016)
- [High-Performance Distributed ML at Scale through Parameter Server Consistency Models](https://mlsanity.com/paper/1410.8043/) (2014)
- [DSP.Ear: Leveraging Co-Processor Support for Continuous Audio Sensing on Smartphones](https://mlsanity.com/paper/1409.3206/) (2014)
- [On nonnegative unbiased estimators](https://mlsanity.com/paper/1309.6473/) (2013)
- [Personal Data: Thinking Inside the Box](https://mlsanity.com/paper/1501.04737/) (2015)
- [Emotion Filtering at the Edge](https://mlsanity.com/paper/1909.08500/) (2019)
- [Glimmers: Resolving the Privacy/Trust Quagmire](https://mlsanity.com/paper/1702.07436/) (2017)
- [Owl: A General-Purpose Numerical Library in OCaml](https://mlsanity.com/paper/1707.09616/) (2017)
- [Probabilistic Synchronous Parallel](https://mlsanity.com/paper/1709.07772/) (2017)

## Abstract (arXiv)

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are a key component of online service providers. The financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance. Deep Learning is the technology behind voice-based personal assistants, etc. Deployment of ML technologies onto cloud computing infrastructures has benefited numerous aspects of our daily life. The advertising and associated online industries in particular have fuelled a rapid rise the in deployment of personal data collection and analytics tools. Traditionally, behavioural analytics relies on collecting vast amounts of data in centralised cloud infrastructure before using it to train machine learning models that allow user behaviour and preferences to be inferred. A contrasting approach, distributed data analytics, where code and models for training and inference are distributed to the places where data is collected, has been boosted by two recent, ongoing developments: increased processing power and memory capacity available in user devices at the edge of the network, such as smartphones and home assistants; and increased sensitivity to the highly intrusive nature of many of these devices and services and the attendant demands for improved privacy. Indeed, the potential for increased privacy is not the only benefit of distributing data analytics to the edges of the network: reducing the movement of large volumes of data can also improve energy efficiency, helping to ameliorate the ever increasing carbon footprint of our digital infrastructure, enabling much lower latency for service interactions than is possible when services are cloud-hosted. These approaches often introduce challenges in privacy, utility, and efficiency trade-offs, while having to ensure fruitful user engagement.