# Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis

arXiv: https://arxiv.org/abs/2605.07888 · HTML: https://mlsanity.com/paper/2605.07888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 28 of 28)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction](https://mlsanity.com/paper/2203.11751/) (2022)
- [SCAFFOLD: Stochastic Controlled Averaging for Federated Learning](https://mlsanity.com/paper/1910.06378/) (2019)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [Federated Learning from Pre-Trained Models: A Contrastive Learning Approach](https://mlsanity.com/paper/2209.10083/) (2022)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [Hard negative examples are hard, but useful](https://mlsanity.com/paper/2007.12749/) (2020)
- [FedProc: Prototypical Contrastive Federated Learning on Non-IID data](https://mlsanity.com/paper/2109.12273/) (2021)
- [Tackling Data Heterogeneity in Federated Learning with Class Prototypes](https://mlsanity.com/paper/2212.02758/) (2022)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [Federated Minimax Optimization: Improved Convergence Analyses and Algorithms](https://mlsanity.com/paper/2203.04850/) (2022)
- [Federated Contrastive Learning for Decentralized Unlabeled Medical Images](https://mlsanity.com/paper/2109.07504/) (2021)
- [Relaxed Contrastive Learning for Federated Learning](https://mlsanity.com/paper/2401.04928/) (2024)
- [Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks](https://mlsanity.com/paper/2209.15595/) (2022)
- [Vision Transformers in 2022: An Update on Tiny ImageNet](https://mlsanity.com/paper/2205.10660/) (2022)
- [Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data](https://mlsanity.com/paper/2403.16398/) (2024)
- [FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models](https://mlsanity.com/paper/2501.02461/) (2025)
- [A Mutual Information Perspective on Federated Contrastive Learning](https://mlsanity.com/paper/2405.02081/) (2024)
- [FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization](https://mlsanity.com/paper/2304.05824/) (2023)
- [Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients](https://mlsanity.com/paper/2503.09206/) (2025)
- [A New Theoretical Perspective on Data Heterogeneity in Federated Optimization](https://mlsanity.com/paper/2407.15567/) (2024)
- [Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation](https://mlsanity.com/paper/2506.20431/) (2025)
- [FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity](https://mlsanity.com/paper/2509.04107/) (2025)

## Abstract (arXiv)

Federated Learning (FL) enables decentralised model training across distributed clients without requiring data centralisation. However, the generalisation performance of the global model is usually degraded by data heterogeneity across clients, particularly under limited data availability and class imbalance. To address this challenge, we propose FedQuad, a novel method that explicitly enforces minimising intra-class representations while enabling inter-class splits across clients. By jointly minimising distances between positive pairs and maximising distances between negative pairs, the proposed approach mitigates representation misalignment introduced during model aggregation. We evaluate our method on CIFAR-10, CIFAR-100, and Tiny-ImageNet under diverse non-IID settings and varying numbers of clients, demonstrating consistent improvements over existing baselines. Additionally, we provide a comprehensive analysis of metric learning-based approaches in both centralised and federated environments, highlighting their effectiveness in alleviating representation collapse under heterogeneous data distributions.