We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sheetalreddy/Three-Approaches-for-Personalization-with-Applications-to-FederatedLearning | pwc_unofficial | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| client_data | Not yet run | sheetalreddy/Three-Approaches-for-Personalization-with-Applications-to-FederatedLearning/src/code_tff.py code served (permissive licence) · get_code("20372f1649e8c7e3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model per user. In this work, we present a systematic learning-theoretic study of personalization. We propose and analyze three approaches: user clustering, data interpolation, and model interpolation. For all three approaches, we provide learning-theoretic guarantees and efficient algorithms for which we also demonstrate the performance empirically. All of our algorithms are model-agnostic and work for any hypothesis class.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.10619")
get_code_for_paper("2002.10619")
have("2002.10619")
Connect an agent — have() is free.