# FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization

arXiv: https://arxiv.org/abs/2304.05824 · HTML: https://mlsanity.com/paper/2304.05824/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [FNBench: Benchmarking Robust Federated Learning against Noisy Labels](https://mlsanity.com/paper/2505.06684/) (2025)
- [FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning](https://mlsanity.com/paper/2409.12105/) (2024)
- [Learnable Sparse Customization in Heterogeneous Edge Computing](https://mlsanity.com/paper/2412.07216/) (2024)
- [FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity](https://mlsanity.com/paper/2509.04107/) (2025)
- [Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis](https://mlsanity.com/paper/2605.07888/) (2026)
- [A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions](https://mlsanity.com/paper/2605.11010/) (2026)

## Abstract (arXiv)

In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. However, these methods impose limitations on the ability to explore superior local models and ignore the valuable information in historical models. Besides, although the up-to-date representation method simultaneously concerns the global and historical local models, it suffers from unbearable computation cost. To accelerate convergence with low resource consumption, we innovatively propose a model regularization method named FedTrip, which is designed to restrict global-local divergence and decrease current-historical correlation for alleviating the negative effects derived from data heterogeneity. FedTrip helps the current local model to be close to the global model while keeping away from historical local models, which contributes to guaranteeing the consistency of local updates among clients and efficiently exploring superior local models with negligible additional computation cost on attaching operations. Empirically, we demonstrate the superiority of FedTrip via extensive evaluations. To achieve the target accuracy, FedTrip outperforms the state-of-the-art baselines in terms of significantly reducing the total overhead of client-server communication and local computation.