# Fast-Convergent Federated Learning with Adaptive Weighting

arXiv: https://arxiv.org/abs/2012.00661 · HTML: https://mlsanity.com/paper/2012.00661/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 30 of 67)

- [Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data](https://mlsanity.com/paper/2105.07066/) (2021)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Accelerated Federated Learning with Decoupled Adaptive Optimization](https://mlsanity.com/paper/2207.07223/) (2022)
- [Prototype Guided Federated Learning of Visual Feature Representations](https://mlsanity.com/paper/2105.08982/) (2021)
- [Can Textual Gradient Work in Federated Learning?](https://mlsanity.com/paper/2502.19980/) (2025)
- [Task Arithmetic Through The Lens Of One-Shot Federated Learning](https://mlsanity.com/paper/2411.18607/) (2024)
- [Federated Face Recognition](https://mlsanity.com/paper/2105.02501/) (2021)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving](https://mlsanity.com/paper/2409.19560/) (2024)
- [FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning](https://mlsanity.com/paper/2208.02442/) (2022)
- [Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation](https://mlsanity.com/paper/2506.12213/) (2025)
- [Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization](https://mlsanity.com/paper/2207.10751/) (2022)
- [Federated Learning with Server Learning: Enhancing Performance for Non-IID Data](https://mlsanity.com/paper/2210.02614/) (2022)
- [Blockchain-enabled Trustworthy Federated Unlearning](https://mlsanity.com/paper/2401.15917/) (2024)
- [CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization](https://mlsanity.com/paper/2302.10413/) (2023)
- [Federated Skewed Label Learning with Logits Fusion](https://mlsanity.com/paper/2311.08202/) (2023)
- [A Thorough Assessment of the Non-IID Data Impact in Federated Learning](https://mlsanity.com/paper/2503.17070/) (2025)
- [Matching DNN Compression and Cooperative Training with Resources and Data Availability](https://mlsanity.com/paper/2212.02304/) (2022)
- [Efficient Fully Distributed Federated Learning with Adaptive Local Links](https://mlsanity.com/paper/2203.12281/) (2022)
- [Adaptive Federated Learning via New Entropy Approach](https://mlsanity.com/paper/2303.14966/) (2023)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [FedCode: Communication-Efficient Federated Learning via Transferring Codebooks](https://mlsanity.com/paper/2311.09270/) (2023)
- [An Element-Wise Weights Aggregation Method for Federated Learning](https://mlsanity.com/paper/2404.15919/) (2024)
- [On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks](https://mlsanity.com/paper/2404.18519/) (2024)
- [Optimizing Federated Learning by Entropy-Based Client Selection](https://mlsanity.com/paper/2411.01240/) (2024)
- [FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity](https://mlsanity.com/paper/2211.07248/) (2022)
- [A Federated Learning Benchmark for Drug-Target Interaction](https://mlsanity.com/paper/2302.07684/) (2023)
- [Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network](https://mlsanity.com/paper/2311.10002/) (2023)
- [Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2402.04409/) (2024)
- [SignSGD with Federated Voting](https://mlsanity.com/paper/2403.16372/) (2024)

## Abstract (arXiv)

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose Federated Adaptive Weighting (FedAdp) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. We observe the implicit connection between the node contribution to the global model aggregation and data distribution on the local node through theoretical and empirical analysis. We then propose to assign different weights for updating the global model based on node contribution adaptively through each training round. The contribution of participating nodes is first measured by the angle between the local gradient vector and the global gradient vector, and then, weight is quantified by a designed non-linear mapping function subsequently. The simple yet effective strategy can reinforce positive (suppress negative) node contribution dynamically, resulting in communication round reduction drastically. Its superiority over the commonly adopted Federated Averaging (FedAvg) is verified both theoretically and experimentally. With extensive experiments performed in Pytorch and PySyft, we show that FL training with FedAdp can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset, as compared to FedAvg algorithm.