Papers › RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

24 May 2024arXiv:2405.15182archive 2025-07-28

Peihua Mai, Ran Yan, Yan Pang

Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg) is often used to obtain the aggregation of gradients on sever without inspecting individual user updates. Unfortunately, existing defense strategies against poisoning attacks rely on the analysis of local updates in plaintext, making them incompatible with SecAgg. To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLPA) based on SecAgg protocol. Our framework computes the cosine similarity between local updates and server updates to conduct robust aggregation. Furthermore, we leverage verifiable packed Shamir secret sharing to achieve reduced communication cost of O(M+N) per user, and design a novel dot product aggregation algorithm to resolve the issue of increased information leakage. Our experimental results show that RFLPA significantly reduces communication and computation overhead by over 75% compared to the state-of-the-art secret sharing method, BREA, while maintaining competitive accuracy.

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Code

Syntology Ran 7 of 8 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 4 ran · honoured contract; 1 ran · violated contract; 2 ran with no contract checked.

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nusioraprivacy/rflpa officialmentioned in paperpytorch report

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Code Syntology ran Syntology

8 samples harvested; 7 ran; 4 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

4ran · honoured contract
1ran · violated contract
2ran
1unverified

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identitymatrix nusioraprivacy/rflpa/utils/crypto_utils.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · dd10a672765762a0 · report
generate_A NusIoraPrivacy/RFLPA/utils/attack_utils.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · 2bc4e9226bc474a4 · report
generate_T NusIoraPrivacy/RFLPA/utils/attack_utils.py official repository ran · honoured contract no licence file found · pointer only · bdc6248e2318b5e1 · report
iid_partition NusIoraPrivacy/RFLPA/utils/data_utils.py official repository ran no licence file found · pointer only · 9186b51e0e017334 · report
make_random_shares nusioraprivacy/rflpa/utils/crypto_utils.py official repository ran · honoured contract fingerprinted no licence file found · pointer only · c016c8036f5ce167 · report
non_iid_partition NusIoraPrivacy/RFLPA/utils/data_utils.py official repository ran no licence file found · pointer only · e2e4f12c8f12ccf9 · report
relative_err NusIoraPrivacy/RFLPA/utils/attack_utils.py official repository ran · violated contract fingerprinted no licence file found · pointer only · 9d4a6861f6edf824 · report
get_dataset NusIoraPrivacy/RFLPA/utils/data_utils.py official repository unverified no licence file found · pointer only · 2b8318c9b2f3a840 · report

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