Zeroth-order optimization meets human feedback: Provable learning via ranking oracles
In this study, we delve into an emerging optimization challenge involving a black-box
objective function that can only be gauged via a ranking oracle-a situation frequently …
objective function that can only be gauged via a ranking oracle-a situation frequently …
Enhancing federated semi-supervised learning with out-of-distribution filtering amidst class mismatches
Federated Learning (FL) has gained prominence as a method for training models on edge
computing devices, enabling the preservation of data privacy by eliminating the need to …
computing devices, enabling the preservation of data privacy by eliminating the need to …
Fedlion: Faster adaptive federated optimization with fewer communication
In Federated Learning (FL), a framework to train machine learning models across distributed
data, well-known algorithms like FedAvg tend to have slow convergence rates, resulting in …
data, well-known algorithms like FedAvg tend to have slow convergence rates, resulting in …
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
The success of current Large-Language Models (LLMs) hinges on extensive training data
that is collected and stored centrally, called Centralized Learning (CL). However, such a …
that is collected and stored centrally, called Centralized Learning (CL). However, such a …
UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation
In the field of medical image compression, Implicit Neural Representation (INR) networks
have shown remarkable versatility due to their flexible compression ratios, yet they are …
have shown remarkable versatility due to their flexible compression ratios, yet they are …
Real-Time pill identification for the visually impaired using deep learning
B Dang, W Zhao, Y Li, D Ma, Q Yu, EY Zhu - arXiv preprint arXiv …, 2024 - arxiv.org
The prevalence of mobile technology offers unique opportunities for addressing healthcare
challenges, especially for individuals with visual impairments. This paper explores the …
challenges, especially for individuals with visual impairments. This paper explores the …
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
ASD Neto, M Egger, M Bakshi, R Bitar - arXiv preprint arXiv:2406.14362, 2024 - arxiv.org
We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-
communication efficient Federated Learning, resilient to Byzantine faults. We show through …
communication efficient Federated Learning, resilient to Byzantine faults. We show through …
Advances in Robust Federated Learning: Heterogeneity Considerations
C Chen, T Liao, X Deng, Z Wu, S Huang… - arXiv preprint arXiv …, 2024 - arxiv.org
In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …
collaboratively train models across multiple clients with different data distributions, model …
Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
J Zhang, J Cao, J Chang, X Li, H Liu, Z Li - arXiv preprint arXiv …, 2024 - arxiv.org
This research aims to explore the application of deep learning in autonomous driving
computer vision technology and its impact on improving system performance. By using …
computer vision technology and its impact on improving system performance. By using …
Skin Cancer Detection Based on Machine Learning
Skin cancer, particularly melanoma, poses a significant health risk, accounting for the
majority of skin cancer-related fatalities in the United States. Despite representing a small …
majority of skin cancer-related fatalities in the United States. Despite representing a small …