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Found 9 papers, 5 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.
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.
FedRule: Federated Rule Recommendation System with Graph Neural Networks
Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door.
Faithful Explanations for Deep Graph Models
Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful.
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.
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs
These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels.
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
