# Budgeted Online Model Selection and Fine-Tuning via Federated Learning

arXiv: https://arxiv.org/abs/2401.10478 · HTML: https://mlsanity.com/paper/2401.10478/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 63 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 30 of 36)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Personalized Federated Learning with First Order Model Optimization](https://mlsanity.com/paper/2012.08565/) (2020)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [Lower Bounds and Optimal Algorithms for Personalized Federated Learning](https://mlsanity.com/paper/2010.02372/) (2020)
- [LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://mlsanity.com/paper/1805.09965/) (2018)
- [Online Learning with Feedback Graphs: Beyond Bandits](https://mlsanity.com/paper/1502.07617/) (2015)
- [Model Selection in Contextual Stochastic Bandit Problems](https://mlsanity.com/paper/2003.01704/) (2020)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Model selection for contextual bandits](https://mlsanity.com/paper/1906.00531/) (2019)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Parameter-free online learning via model selection](https://mlsanity.com/paper/1801.00101/) (2017)
- [Online Model Selection for Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2011.09750/) (2020)
- [Online Learning with Feedback Graphs Without the Graphs](https://mlsanity.com/paper/1605.07018/) (2016)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Online Active Model Selection for Pre-trained Classifiers](https://mlsanity.com/paper/2010.09818/) (2020)
- [Communication-Efficient Online Federated Learning Framework for Nonlinear Regression](https://mlsanity.com/paper/2110.06556/) (2021)
- [Best of Both Worlds Model Selection](https://mlsanity.com/paper/2206.14912/) (2022)
- [Personalized Online Federated Learning with Multiple Kernels](https://mlsanity.com/paper/2311.05108/) (2023)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)

## Built on by (most cited first, 1 of 1)

- [Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning](https://mlsanity.com/paper/2410.21547/) (2024)

## Abstract (arXiv)

Online model selection involves selecting a model from a set of candidate models 'on the fly' to perform prediction on a stream of data. The choice of candidate models henceforth has a crucial impact on the performance. Although employing a larger set of candidate models naturally leads to more flexibility in model selection, this may be infeasible in cases where prediction tasks are performed on edge devices with limited memory. Faced with this challenge, the present paper proposes an online federated model selection framework where a group of learners (clients) interacts with a server with sufficient memory such that the server stores all candidate models. However, each client only chooses to store a subset of models that can be fit into its memory and performs its own prediction task using one of the stored models. Furthermore, employing the proposed algorithm, clients and the server collaborate to fine-tune models to adapt them to a non-stationary environment. Theoretical analysis proves that the proposed algorithm enjoys sub-linear regret with respect to the best model in hindsight. Experiments on real datasets demonstrate the effectiveness of the proposed algorithm.