# Federated Meta-Learning with Fast Convergence and Efficient Communication

arXiv: https://arxiv.org/abs/1802.07876 · HTML: https://mlsanity.com/paper/1802.07876/

## Key facts

- Cited by 155 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 30 of 155)

- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [Personalized Federated Learning via Variational Bayesian Inference](https://mlsanity.com/paper/2206.07977/) (2022)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [What Do We Mean by Generalization in Federated Learning?](https://mlsanity.com/paper/2110.14216/) (2021)
- [Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning](https://mlsanity.com/paper/2110.08394/) (2021)
- [Cluster-driven Graph Federated Learning over Multiple Domains](https://mlsanity.com/paper/2104.14628/) (2021)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [Test-Time Robust Personalization for Federated Learning](https://mlsanity.com/paper/2205.10920/) (2022)
- [Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective](https://mlsanity.com/paper/2111.03741/) (2021)
- [Eliminating Domain Bias for Federated Learning in Representation Space](https://mlsanity.com/paper/2311.14975/) (2023)
- [Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques](https://mlsanity.com/paper/2102.09743/) (2021)
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://mlsanity.com/paper/2105.03170/) (2021)
- [On the Convergence of Clustered Federated Learning](https://mlsanity.com/paper/2202.06187/) (2022)
- [Motley: Benchmarking Heterogeneity and Personalization in Federated Learning](https://mlsanity.com/paper/2206.09262/) (2022)
- [Personalized Federated Learning With Graph](https://mlsanity.com/paper/2203.00829/) (2022)
- [Benchmarking Differential Privacy and Federated Learning for BERT Models](https://mlsanity.com/paper/2106.13973/) (2021)
- [Federated Mixture of Experts](https://mlsanity.com/paper/2107.06724/) (2021)
- [Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring](https://mlsanity.com/paper/2206.06818/) (2022)
- [Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity](https://mlsanity.com/paper/2105.00562/) (2021)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning](https://mlsanity.com/paper/2404.02478/) (2024)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)

## Abstract (arXiv)

Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers.