Summary of Fedmes: Personalized Federated Continual Learning Leveraging Local Memory, by Jin Xie et al.
FedMeS: Personalized Federated Continual Learning Leveraging Local Memory
by Jin Xie, Chenqing Zhu, Songze Li
First submitted to arxiv on: 19 Apr 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 A novel framework called Federated Memory Strengthening (FedMeS) is proposed for Personalized Federated Continual Learning (PFCL), addressing challenges of client drift and catastrophic forgetting. PFCL involves a central server coordinating distributed clients with local tasks on arbitrary data distributions, requiring personalized models to perform well on all tasks. FedMeS leverages a small amount of local memory to store samples from previous tasks, calibrating gradient updates and facilitating personalization via KNN-based Gaussian inference. This task-oblivious approach is theoretically analyzed and experimentally evaluated, outperforming baselines in average accuracy and forgetting rate across various datasets, task distributions, and client numbers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps devices learn together without sharing data. Imagine you’re trying to teach a model on many different devices, each with its own data. The model needs to be good at all the tasks, but it can forget what it learned from previous tasks. A new way called Federated Memory Strengthening (FedMeS) makes sure the model remembers and improves over time. It stores important information from past tasks and uses that to help it learn better. This approach works well and is better than other methods in many situations. |
Keywords
» Artificial intelligence » Continual learning » Federated learning » Inference
