Distributed training security and privacy
Newest first · 1 reviewed on this page
Search insteadShunan Zhu, Jiawei Chen, Yonghao Yu +1
As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving privacy. However, real-world client data often contains toxic or unsafe information. This leads to a critical issue we define as…
Oğuzhan Ersoy, Nikolay Blagoev, Jona te Lintelo +3
Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training can be vulnerable to poisoning and backdoor attacks by one or more malicious participants. There…
Youqian Zhang
This work examines an imbalance in artificial intelligence (AI) security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including…
Chenqing Zhu, Yanbo Dai, Yulong Tian +2
Large Language Model (LLM)-based question-answering (QA) systems are increasingly deployed in sensitive domains such as healthcare, mental health counseling, and legal consultation. Federated learning (FL) enables collaborative training without sharing raw client data, for which…
Jinxi Yu, Eric Hanchen Jiang, Levina Li +6
Topology-guided safeguards for LLM-based multi-agent systems (MAS) train a GNN over the inter-agent communication graph to localize risky agents and intervene on the topology---but they assume one operator can pool all labeled traces. Across organizations that assumption breaks:…
Ali Akarma, Toqeer Ali Syed, Muhammad Khan +2
As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and infrastructure to train shared models without exposing raw sensor data. Yet the updates clients transmit still leak enough…
Jordi Luque, Fernando López, Aleix Sant
Per-layer differential privacy (DP) clipping improves gradient fidelity in federated learning by allocating per-matrix clipping budgets proportional to parameter count. We show that this recipe breaks for speech large language models (speech-LLMs), when the acoustic encoder and…
Hao-Cheng Jiang, Hua Shen
As large language models are increasingly adopted in federated learning, protecting user privacy while performing parameter-efficient fine-tuning on distributed private data has become an important challenge. Although clients only share gradients instead of directly uploading…
Wei Sun, Xianda Wang, Zhicheng Liang +5
Federated learning (FL) stands out as a promising solution to address the ever-increasing data scarcity problem of large language model (LLM) training through collecting data from distributed sources. The unique challenges however exist in three aspects, the extreme large…
Mohammed Firdos Alam Sheikh
Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables multiple clients to collaboratively train a global model without sharing raw data, thereby preserving data privacy. . In adversarial settings, malicious clients can inject carefully…
Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang +2
Examines federated learning approaches for fine-tuning LLMs on edge devices, analyzing privacy guarantees, communication efficiency, and security trade-offs.