Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918219126669312 |
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| author | Xu, Shuangqing Zheng, Yifeng Hua, Zhongyun |
| author_facet | Xu, Shuangqing Zheng, Yifeng Hua, Zhongyun |
| contents | Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high-dimensional gradient updates from many clients, FL is known to suffer from a communication bottleneck. Meanwhile, the gradients shared by clients as well as the trained model may also be exploited for inferring private local datasets, making privacy still a critical concern in FL. We present Clover, a novel system framework for communication-efficient, secure, and differentially private FL. To tackle the communication bottleneck in FL, Clover follows a standard and commonly used approach-top-k gradient sparsification, where each client sparsifies its gradient update such that only k largest gradients (measured by magnitude) are preserved for aggregation. Clover provides a tailored mechanism built out of a trending distributed trust setting involving three servers, which allows to efficiently aggregate multiple sparse vectors (top-k sparsified gradient updates) into a dense vector while hiding the values and indices of non-zero elements in each sparse vector. This mechanism outperforms a baseline built on the general distributed ORAM technique by several orders of magnitude in server-side communication and runtime, with also smaller client communication cost. We further integrate this mechanism with a lightweight distributed noise generation mechanism to offer differential privacy (DP) guarantees on the trained model. To harden Clover with security against a malicious server, we devise a series of lightweight mechanisms for integrity checks on the server-side computation. Extensive experiments show that Clover can achieve utility comparable to vanilla FL with central DP, with promising performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization Xu, Shuangqing Zheng, Yifeng Hua, Zhongyun Cryptography and Security Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high-dimensional gradient updates from many clients, FL is known to suffer from a communication bottleneck. Meanwhile, the gradients shared by clients as well as the trained model may also be exploited for inferring private local datasets, making privacy still a critical concern in FL. We present Clover, a novel system framework for communication-efficient, secure, and differentially private FL. To tackle the communication bottleneck in FL, Clover follows a standard and commonly used approach-top-k gradient sparsification, where each client sparsifies its gradient update such that only k largest gradients (measured by magnitude) are preserved for aggregation. Clover provides a tailored mechanism built out of a trending distributed trust setting involving three servers, which allows to efficiently aggregate multiple sparse vectors (top-k sparsified gradient updates) into a dense vector while hiding the values and indices of non-zero elements in each sparse vector. This mechanism outperforms a baseline built on the general distributed ORAM technique by several orders of magnitude in server-side communication and runtime, with also smaller client communication cost. We further integrate this mechanism with a lightweight distributed noise generation mechanism to offer differential privacy (DP) guarantees on the trained model. To harden Clover with security against a malicious server, we devise a series of lightweight mechanisms for integrity checks on the server-side computation. Extensive experiments show that Clover can achieve utility comparable to vanilla FL with central DP, with promising performance. |
| title | Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2511.07123 |