TrajSyn: Privacy-Preserving Dataset Distillation from Federated Model Trajectories for Server-Side Adversarial Training

• URL: http://arxiv.org/abs/2512.15123v1
• Date: Wed, 17 Dec 2025 06:29:43 GMT
• Title: TrajSyn: Privacy-Preserving Dataset Distillation from Federated Model Trajectories for Server-Side Adversarial Training
• Abstract summary: adversarial training is a strong defense, but it is difficult to apply in Federated Learning.<n>In this work, we introduce TrajSyn, a privacy-preserving framework that enables effective server-side adversarial training.<n>We show that TrajSyn consistently improves adversarial robustness on image classification benchmarks with no extra compute burden on the client device.
• Score: 2.5296581087709127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models deployed on edge devices are increasingly used in safety-critical applications. However, their vulnerability to adversarial perturbations poses significant risks, especially in Federated Learning (FL) settings where identical models are distributed across thousands of clients. While adversarial training is a strong defense, it is difficult to apply in FL due to strict client-data privacy constraints and the limited compute available on edge devices. In this work, we introduce TrajSyn, a privacy-preserving framework that enables effective server-side adversarial training by synthesizing a proxy dataset from the trajectories of client model updates, without accessing raw client data. We show that TrajSyn consistently improves adversarial robustness on image classification benchmarks with no extra compute burden on the client device.

Related papers

• FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation [7.120183076762444]
  Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data.<n>Recent studies show that conventional FL algorithms still exhibit deficiencies in privacy protection.<n>We propose FedFG, a robust FL framework based on flow-matching generation that simultaneously preserves client privacy and resists sophisticated poisoning attacks.
  arXiv  Detail & Related papers  (2026-03-30T03:11:35Z)
• ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version) [58.595691399741646]
  Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs)<n>This setup enables SL to leverage server capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data.<n>We present ZORRO, a private, verifiable, and robust SL defense scheme.
  arXiv  Detail & Related papers  (2025-09-11T18:44:09Z)
• Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices [12.709837838251952]
  We propose a new federated learning scheme on edge devices called Federated Learning with Encrypted Data Sharing.<n>FedEDS uses the client model and the model's layer to train the data encryptor and share it with other clients.<n>This approach accelerates the convergence speed of federated learning training and mitigates the negative impact of data heterogeneity.
  arXiv  Detail & Related papers  (2025-06-25T17:40:54Z)
• Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates [36.2911560725828]
  Federated learning (FL) enables decentralized machine learning without sharing raw data.<n>Privacy leakage is possible under commonly adopted FL protocols.<n>We introduce a novel threat model in FL, named the maliciously curious client.
  arXiv  Detail & Related papers  (2025-06-13T02:23:41Z)
• Byzantine-Robust Federated Learning Using Generative Adversarial Networks [1.4091801425319963]
  Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, but its robustness is threatened by Byzantine behaviors such as data and model poisoning.<n>We present a defense framework that addresses these challenges by leveraging a conditional generative adversarial network (cGAN) at the server to synthesize representative data for validating client updates.<n>This approach eliminates reliance on external datasets, adapts to diverse attack strategies, and integrates seamlessly into standard FL.
  arXiv  Detail & Related papers  (2025-03-26T18:00:56Z)
• ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks [26.002975401820887]
  Federated Learning (FL) is a distributed learning framework designed for privacy-aware applications. Traditional FL approaches risk exposing sensitive client data when plain model updates are transmitted to the server. Google's Secure Aggregation (SecAgg) protocol addresses this threat by employing a double-masking technique. We propose ACCESS-FL, a communication-and-computation-efficient secure aggregation method.
  arXiv  Detail & Related papers  (2024-09-03T09:03:38Z)
• Trust Driven On-Demand Scheme for Client Deployment in Federated Learning [39.9947471801304]
  "Trusted-On-Demand-FL" establishes a relationship of trust between the server and the pool of eligible clients. Our simulations rely on a continuous user behavior dataset, deploying an optimization model powered by a genetic algorithm.
  arXiv  Detail & Related papers  (2024-05-01T08:50:08Z)
• Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks [48.70867241987739]
  InferGuard is a novel Byzantine-robust aggregation rule aimed at defending against client-side training data distribution inference attacks. The results of our experiments indicate that our defense mechanism is highly effective in protecting against client-side training data distribution inference attacks.
  arXiv  Detail & Related papers  (2024-03-05T17:41:35Z)
• Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
  Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
  arXiv  Detail & Related papers  (2023-09-14T03:48:27Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.