# EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters

arXiv: https://arxiv.org/abs/2406.09152 · HTML: https://mlsanity.com/paper/2406.09152/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Sparse Communication for Distributed Gradient Descent](https://mlsanity.com/paper/1704.05021/) (2017)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [Characterising Bias in Compressed Models](https://mlsanity.com/paper/2010.03058/) (2020)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [A Framework for Evaluating Gradient Leakage Attacks in Federated Learning](https://mlsanity.com/paper/2004.10397/) (2020)
- [EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning](https://mlsanity.com/paper/2108.08842/) (2021)
- [HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1912.05897/) (2019)
- [Masked Training of Neural Networks with Partial Gradients](https://mlsanity.com/paper/2106.08895/) (2021)
- [DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting](https://mlsanity.com/paper/2207.07779/) (2022)
- [HEAX: An Architecture for Computing on Encrypted Data](https://mlsanity.com/paper/1909.09731/) (2019)
- [FRL: Federated Rank Learning](https://mlsanity.com/paper/2110.04350/) (2021)
- [Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation](https://mlsanity.com/paper/2401.14211/) (2024)
- [Binary Fuse Filters: Fast and Smaller Than Xor Filters](https://mlsanity.com/paper/2201.01174/) (2022)
- [FedCode: Communication-Efficient Federated Learning via Transferring Codebooks](https://mlsanity.com/paper/2311.09270/) (2023)

## Built on by (most cited first, 3 of 3)

- [EFU: Enforcing Federated Unlearning via Functional Encryption](https://mlsanity.com/paper/2508.07873/) (2025)
- [Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques](https://mlsanity.com/paper/2603.05158/) (2026)
- [DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs](https://mlsanity.com/paper/2604.19118/) (2026)

## Abstract (arXiv)

Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerable to inference attacks during model update transmissions. Existing secure aggregation approaches rely on differential privacy or cryptographic schemes like Functional Encryption (FE) to safeguard individual client data. However, such strategies can reduce performance or introduce unacceptable computational and communication overheads on clients running on edge devices with limited resources. In this work, we present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding using probabilistic filters to deliver strong privacy guarantees in FL without affecting model performance or adding unnecessary burdens to clients. We performed a comprehensive evaluation, spanning various datasets and architectures, to demonstrate EncCluster's scalability across encryption levels. Our findings reveal that EncCluster significantly reduces communication costs - below even conventional FedAvg - and accelerates encryption by more than four times over all baselines; at the same time, it maintains high model accuracy and enhanced privacy assurances.