Abstract Model fusion without accessing training data in machine learning has attracted
increasing interest due to the practical resource-saving and data privacy issues. During the …

Combining different models is a widely used paradigm in machine learning applications.
While the most common approach is to form an ensemble of models and average their …

The rapid expansion of the open-source language model landscape presents an opportunity
to merge the competencies of these model checkpoints by combining their parameters …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

This study was conducted to find out what has not been discussed in last research in domain
text mining and NLP using Deep Learning. In this literature review has covered more than …

Ethics Guidelines for Trustworthy AI advocate for AI technology that is, among other things,
more inclusive. Explainable AI (XAI) aims at making state of the art opaque models more …

Federated learning learns from scattered data by fusing collaborative models from local
nodes. However, due to chaotic information distribution, the model fusion may suffer from …

Low-Rank Adaptation (LoRA) is a popular technique for parameter-efficient fine-tuning of
Large Language Models (LLMs). We study how different LoRA modules can be merged to …

Incorporation of new knowledge into neural networks with simultaneous preservation of the
previous knowledge is known to be a nontrivial problem. This problem becomes even more …

Personalized federated learning (PFL) is a framework that targets individual models for
optimization, providing better privacy and flexibility for clients. However, in challenging …