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- Marimuthu Swami PalaniswamiSuggested from co-authorship
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Works208 from public data
- Communication-efficient federated learning via knowledge distillation634
This work presents a communication-efficient federated learning method that saves a major fraction of communication cost, and reveals the advantage of reciprocal learning in machine knowledge transfer and the evolutional low-rank properties of deep model updates.
- 593
- Privacy and Robustness in Federated Learning: Attacks and Defenses582
A comprehensive survey on privacy and robustness in FL over the past five years is conducted and a concise introduction to the concept of FL is provided and a unique taxonomy covering privacy attacks and defenses is provided.
- Defending ChatGPT against jailbreak attack via self-reminders515
This work systematically documents the threats posed by jailbreak attacks, introduces and analyses a dataset for evaluating defensive interventions and proposes the psychologically inspired self-reminder technique that can efficiently and effectively mitigate against jailbreaks without further training.
- Local Differential Privacy-Based Federated Learning for Internet of Things449
This article proposes to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model, and proposes four LDP mechanisms to perturb gradients generated by vehicles.
- 434
- A federated graph neural network framework for privacy-preserving personalization347
A federated GNN framework named FedPerGNN, which can collaboratively train GNN models based on decentralized graphs inferred from local data and introduces a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection.
- Data Poisoning Attacks on Federated Machine Learning310
This work attempts to attacking a popular federated multitask learning framework, which uses a general multitask learning framework to handle statistical challenges in the federated learning setting, and proposes a novel systems-aware optimization method, called as attack on federated learning (AT2FL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning.
- Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information287
An algorithm to launch clean-label backdoor attacks using only samples from the target class and public out-of-distribution data is designed and the synthesized Narcissus trigger contains durable features as persistent as the original target class features.
- 255
- 241
- Collaborative Fairness in Federated Learning229
This work investigates the collaborative fairness in FL, and proposes a novel Collaborative Fair Federated Learning (CFFL) framework which utilizes reputation to enforce participants to converge to different models, thus achieving fairness without compromising the predictive performance.
- 220
- FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT209
This article combines federated learning (FL) and fog/edge computing to combat malicious codes and trains a global optimized model based on distributed datasets of collaborators while removing the data and communication constraints.
- FedBERT : When Federated Learning Meets Pre-training182
This work proposes a new learning approach, FedBERT, that takes advantage of the federated learning and split learning approaches, resorting to pre-training BERT in a federated way and can prevent sharing the raw data information and obtain excellent performance.
- Privacy for Free: How does Dataset Condensation Help Privacy?173
This work identifies that dataset condensation (DC) which is originally designed for improving training efficiency is also a better solution to replace the traditional data generators for private data generation, thus providing privacy for free.
- A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment139
This paper introduces the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization, and represents the first safety survey to encompass the entire lifecycle of LLMs.
- 139
- TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation136
A novel method called TARGET (federatTed clAss-continual leaRninG via Exemplar-free disTillation via Exemplar-free disTillation), which alleviates catastrophic forgetting in FCCL while preserving client data privacy.
- Federated Model Distillation with Noise-Free Differential Privacy135
This paper proposes a novel framework called FEDMD-NFDP, which applies a Noise-FreeDifferential Privacy (NFDP) mechanism into a federated model distillation framework, and demonstrates the feasibility of this framework by considering both IID and Non-IID settings, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
- Protecting Intellectual Property of Language Generation APIs with Lexical Watermark134
This work presents a novel watermarking method for text generation APIs by conducting lexical modification to the original outputs that achieves better identifiable performance in terms of p-value, with fewer semantic losses.
- Detecting, Explaining, and Mitigating Memorization in Diffusion Models133
This work introduces a straightforward yet effective method for detecting memorized prompts by inspecting the magnitude of text-conditional predictions, and proposes two strategies to mitigate memorization by leveraging the magnitude of text-conditional predictions, either through minimization during inference or filtering during training.
- Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification132
This paper proposes VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models.
- Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!125
This work presents how an adversary can steal a BERT-based API service (the victim/target model) on multiple benchmark datasets with limited prior knowledge and queries, and shows that the extracted model can lead to highly transferable adversarial attacks against the victim model.
- Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering118
This work proposes a novel anomaly detection method, called Fog-Empowered anomaly detection, by harnessing the processing power of the Fog computing platform and using an efficient hyperellipsoidal clustering algorithm to achieve anomaly detection in a timely manner.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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