# Federated Learning with Nesterov Accelerated Gradient

arXiv: https://arxiv.org/abs/2009.08716 · HTML: https://mlsanity.com/paper/2009.08716/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

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

- [Learning from Future: A Novel Self-Training Framework for Semantic Segmentation](https://mlsanity.com/paper/2209.06993/) (2022)
- [FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients](https://mlsanity.com/paper/2502.20639/) (2025)
- [Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks](https://mlsanity.com/paper/2210.14560/) (2022)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks](https://mlsanity.com/paper/2405.17914/) (2024)
- [Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization](https://mlsanity.com/paper/2405.17932/) (2024)
- [FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2502.21012/) (2025)
- [A Byzantine-Robust Federated Learning with Nesterov Momentum](https://mlsanity.com/paper/2511.02657/) (2025)
- [Generalized Continuous-Time Models for Nesterov's Accelerated Gradient Methods](https://mlsanity.com/paper/2409.00913/) (2024)
- [Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima](https://mlsanity.com/paper/2511.00469/) (2025)
- [SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2511.09828/) (2025)

## Abstract (arXiv)

Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL (FedAvg) employs gradient descent algorithm, which may not be efficient enough. Momentum is able to improve the situation by adding an additional momentum step to accelerate the convergence and has demonstrated its benefits in both centralized and FL environments. It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far. This motives us to propose FedNAG, which employs NAG in each worker as well as NAG momentum and model aggregation in the aggregator. We provide a detailed convergence analysis of FedNAG and compare it with FedAvg. Extensive experiments based on real-world datasets and trace-driven simulation are conducted, demonstrating that FedNAG increases the learning accuracy by 3-24% and decreases the total training time by 11-70% compared with the benchmarks under a wide range of settings.