4 papers
Efficient Decentralized Multi-task Dataset Valuation via Model Merging
Mohammadsajad Alipour, Mohammad Mohammadi Amiri
Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-tas…
Optimal Singular Damage: Efficient LLM Inference in Low Storage Regimes
Mohammadsajad Alipour, Mohammad Mohammadi Amiri
Large language models (LLMs) are increasingly prevalent across diverse applications. However, their enormous size limits storage and processing capabilities to a few well-resourced…
Towards Reversible Model Merging For Low-rank Weights
Mohammadsajad Alipour, Mohammad Mohammadi Amiri
Model merging aims to combine multiple fine-tuned models into a single set of weights that performs well across all source tasks. While prior work has shown that merging can approx…
Power to the Clients: Federated Learning in a Dictatorship Setting
Mohammadsajad Alipour, Mohammad Mohammadi Amiri
Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively learn a shared model without exchanging t…