Dimitris Stripelis
3 papers in the PaperMetrix corpus
Papers by this author
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Accelerating Federated Learning in Heterogeneous Data and Computational Environments
2020 · arXiv (Cornell University)
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. For example, data present in users' cellphones, …
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Scaling Neuroscience Research Using Federated Learning
2021
The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches that require data to be copied to a …
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FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
2025
Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domain-specific, …