Search Results for author:
Found 4 papers, 1 papers with code
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
In the absence of centralized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning.
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the past when trained on new data.
Defending against Poisoning Backdoor Attacks on Federated Meta-learning
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy.
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
A possible solution to this problem is to utilize off-the-shelf sparse learning algorithms at the clients to meet their resource budget.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
