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Found 6 papers, 3 papers with code
LLM Multi-Agent Systems: Challenges and Open Problems
This paper explores existing works of multi-agent systems and identifies challenges that remain inadequately addressed.
Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning
Federated Learning (FL) systems are vulnerable to adversarial attacks, where malicious clients submit poisoned models to prevent the global model from converging or plant backdoors to induce the global model to misclassify some samples.
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
We propose a novel blind annotation protocol based on homomorphic encryption that allows domain oracles to collaboratively label ground truths without sharing data in plaintext with other parties.
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated.
