Hugging Face Trending Papers
On Johnson-Lindenstrauss Transforms for Byzantine-Robust Optimization
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arXiv Machine Learning
Oct 21
On Johnson-Lindenstrauss Transforms for Byzantine-Robust Optimization
The paper investigates the use of Johnson‑Lindenstrauss (JL) transforms in Byzantine‑robust distributed optimization. By leveraging JL’s ability to preserve inner products and distances, worker messages are weighted based on their alignment with a trusted reference gradient, reducing communication costs while maintaining robustness. Experimental results support the theoretical claims.
arXiv Machine Learning
Sep 11
Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
arXiv:2609. 11712v1 Announce Type: cross Abstract: In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function $l_{\sigma}$.
arXiv Machine Learning
Jun 2
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
arXiv Machine Learning
Aug 10
Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.
Hugging Face Trending Papers
Jul 13
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Federated learning distributes data among $n$ clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tackle this issue, centered clipping and Huber aggregators have been exploited for Byzantine robustness.
arXiv Machine Learning
Oct 3
FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization
FedFit introduces a federated fine‑tuning framework for large language models that reduces communication overhead by using a disjoint shared vector‑bank parameterization to reconstruct adapter matrices from two compact global vector banks. It resolves the aggregation dilemma between Sum‑of‑Products and Product‑of‑Sums through an alternating optimization schedule that alternates between accurate single‑bank updates and joint updates corrected by a Residual Spectral Aggregation mechanism. The method also incorporates blockwise quantization with client‑side error feedback and provides theoretical convergence guarantees, achieving perplexity comparable to standard federated LoRA while delivering up to 100× higher compression ratios on Qwen2.5 models.