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Found 10 papers, 3 papers with code
SparsyFed: Sparse Adaptive Federated Training
Sparse training is often adopted in cross-device federated learning (FL) environments where constrained devices collaboratively train a machine learning model on private data by exchanging pseudo-gradients across heterogeneous networks.
BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology
Large Language Models (LLMs) and LLM-based agents show great promise in accelerating scientific research.
LUNAR: LLM Unlearning via Neural Activation Redirection
Large Language Models (LLMs) benefit from training on ever larger amounts of textual data, but as a result, they increasingly incur the risk of leaking private information.
Photon: Federated LLM Pre-Training
Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distributed training.
DEPT: Decoupled Embeddings for Pre-training Language Models
Language model pre-training benefits from diverse data to enhance performance across domains and languages.
Sheaf HyperNetworks for Personalized Federated Learning
Graph hypernetworks (GHNs), constructed by combining graph neural networks (GNNs) with hypernetworks (HNs), leverage relational data across various domains such as neural architecture search, molecular property prediction and federated learning.
Worldwide Federated Training of Language Models
The reliance of language model training on massive amounts of computation and vast datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into question practically, legally, and ethically.
The Future of Large Language Model Pre-training is Federated
We further show the effectiveness of the federated training scales with model size and present our approach for training billion-scale federated LLMs using limited resources.
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
Flower: A Friendly Federated Learning Research Framework
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
