Summary of Task Arithmetic Through the Lens Of One-shot Federated Learning, by Zhixu Tao et al.
Task Arithmetic Through The Lens Of One-Shot Federated Learning
by Zhixu Tao, Ian Mason, Sanjeev Kulkarni, Xavier Boix
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Task Arithmetic is a technique that allows multiple models’ capabilities to be combined into a single model through simple arithmetic in the weight space, without requiring additional fine-tuning or access to original training data. This paper explores Task Arithmetic’s application in multi-task learning by framing it as a one-shot Federated Learning problem. The authors demonstrate that Task Arithmetic is mathematically equivalent to Federated Averaging (FedAvg), a commonly used algorithm in Federated Learning. By leveraging FedAvg’s theoretical results, the study identifies data heterogeneity and training heterogeneity as key factors impacting Task Arithmetic’s performance. To mitigate these challenges, the authors adapt Federated Learning algorithms to improve Task Arithmetic’s effectiveness. Experimental results show that applying these algorithms can significantly boost the merged model’s performance compared to the original Task Arithmetic approach. This work bridges Task Arithmetic and Federated Learning, offering new theoretical insights on Task Arithmetic and practical methodologies for model merging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to combine different models into one super-strong model without needing more training or data. The authors look at how this works when combining multiple tasks together, like adding up lots of math problems. They show that this method is related to another technique called Federated Learning. The researchers found two important things that can make this combination process work better: having different kinds of data and doing the training in different ways. To fix these issues, they use ideas from Federated Learning to improve the results. Their tests showed that using these new methods can really help the combined model be more powerful. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Multi task » One shot
