# Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications

arXiv: https://arxiv.org/abs/2103.13266 · HTML: https://mlsanity.com/paper/2103.13266/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Federated Evaluation of On-device Personalization](https://mlsanity.com/paper/1910.10252/) (2019)
- [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://mlsanity.com/paper/1906.04329/) (2019)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [InstaHide: Instance-hiding Schemes for Private Distributed Learning](https://mlsanity.com/paper/2010.02772/) (2020)
- [Decentralized Collaborative Learning of Personalized Models over Networks](https://mlsanity.com/paper/1610.05202/) (2016)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets](https://mlsanity.com/paper/2008.03371/) (2020)
- [Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks](https://mlsanity.com/paper/2007.09511/) (2020)
- [Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions](https://mlsanity.com/paper/1606.02421/) (2016)
- [Personalized Human Activity Recognition Using Convolutional Neural Networks](https://mlsanity.com/paper/1801.08252/) (2018)
- [Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1303.2837/) (2013)
- [ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification](https://mlsanity.com/paper/1904.03775/) (2019)
- [On Optimal Neighbor Discovery](https://mlsanity.com/paper/1905.05220/) (2019)

## Built on by (most cited first, 11 of 11)

- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones](https://mlsanity.com/paper/2210.16918/) (2022)
- [Personalized Federated Learning with Exact Stochastic Gradient Descent](https://mlsanity.com/paper/2202.09848/) (2022)
- [iDML: Incentivized Decentralized Machine Learning](https://mlsanity.com/paper/2304.05354/) (2023)
- [Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios](https://mlsanity.com/paper/2310.02986/) (2023)
- [Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain](https://mlsanity.com/paper/2207.08180/) (2022)
- [Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles](https://mlsanity.com/paper/2104.09872/) (2021)
- [On the Limit Performance of Floating Gossip](https://mlsanity.com/paper/2302.08413/) (2023)
- [FedSub: Introducing Class-aware Subnetworks Fusion to Enhance Personalized Federated Learning](https://mlsanity.com/paper/2411.08699/) (2024)
- [ML Mule: Mobile-Driven Context-Aware Collaborative Learning](https://mlsanity.com/paper/2501.07536/) (2025)
- [Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints](https://mlsanity.com/paper/2606.28342/) (2026)

## Abstract (arXiv)

Pervasive computing applications commonly involve user's personal smartphones collecting data to influence application behavior. Applications are often backed by models that learn from the user's experiences to provide personalized and responsive behavior. While models are often pre-trained on massive datasets, federated learning has gained attention for its ability to train globally shared models on users' private data without requiring the users to share their data directly. However, federated learning requires devices to collaborate via a central server, under the assumption that all users desire to learn the same model. We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences. However, instead of learning in isolation, these models opportunistically incorporate the learned experiences of other devices they encounter opportunistically. In this paper, we explore the feasibility and limits of such an approach, culminating in a framework that supports encounter-based pairwise collaborative learning. The use of our opportunistic encounter-based learning amplifies the performance of personalized learning while resisting overfitting to encountered data.