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Found 4 papers, 2 papers with code
Privacy-Aware Semantic Cache for Large Language Models
Caching is a natural solution to reduce LLM inference costs on repeated queries which constitute about 31% of the total queries.
ProvFL: Client-Driven Interpretability of Global Model Predictions in Federated Learning
Regardless of the quality of the global model or if it has a fault, understanding the model's origin is equally important for debugging, interpretability, and explainability in federated learning.
FedDefender: Backdoor Attack Defense in Federated Learning
Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e. g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively.
FedDebug: Systematic Debugging for Federated Learning Applications
FedDebug's interactive debugging incurs 1. 2% overhead during training, while it localizes a faulty client in only 2. 1% of a round's training time.
