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Found 11 papers, 2 papers with code
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences.
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
In this paper, we first design a Maverick-aware Shapley valuation that fairly evaluates the contribution of Mavericks.
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods.
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Federated Learning (FL) systems are susceptible to adversarial attacks, where malicious clients submit poisoned models to disrupt the convergence or plant backdoors that cause the global model to misclassify some samples.
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e. g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i. e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design.
Secure Federated Clustering
We consider a foundational unsupervised learning task of $k$-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients.
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Distributed implementations are crucial in speeding up large scale machine learning applications.
Timely Communication in Federated Learning
Under the proposed scheme, at each iteration, the PS waits for $m$ available clients and sends them the current model.
Gradient Coding with Dynamic Clustering for Straggler Mitigation
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest \textit{straggling} workers.
Age-Based Coded Computation for Bias Reduction in Distributed Learning
To mitigate biased estimators, we design a $timely$ dynamic encoding framework for partial recovery that includes an ordering operator that changes the codewords and computation orders at workers over time.
