FedL2P: Federated Learning to Personalize
Authors: Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy Hospedales, Ferenc Huszar, Nicholas Lane
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that this framework improves on a range of standard hand-crafted personalization baselines in both label and feature shift situations.0 |
| Researcher Affiliation | Collaboration | 1 University of Cambridge, UK 2 Samsung AI Center, Cambridge, UK 3 University of Edinburgh, UK 4 Flower Labs |
| Pseudocode | Yes | Algorithm 1 Fed L2P: FL of meta-nets for Personalization Hyperparameters; Algorithm 2 Hypergradient |
| Open Source Code | Yes | Code is available at https://github.com/royson/fedl2p |
| Open Datasets | Yes | CIFAR10 [31]. A widely-used image classification dataset, also popular as an FL benchmark. The number of clients C is set to 1000 and 20% of the training data is used for validation. |
| Dataset Splits | Yes | CIFAR10 [31]. A widely-used image classification dataset, also popular as an FL benchmark. The number of clients C is set to 1000 and 20% of the training data is used for validation. |
| Hardware Specification | Yes | We use the Flower federated learning framework [6] and 8 NVIDIA Ge Force RTX 2080 Ti GPUs for all experiments. |
| Software Dependencies | No | The paper mentions 'Flower federated learning framework' and 'torchvision [41]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The learning rate (ζ) for λ = {wbn, wlr, η} is set to {10 3,10 3,10 4}, respectively. The hypergradient is clipped by value [ 1, 1], Q = 3, and ψ = 0.1 in Alg. 2. The maximum number of communication rounds is set to 500, and over the rounds we save the λ value that leads to the lowest validation loss, averaged over the participating clients, as the final learned λ. The fraction ratio r=0.1 unless stated otherwise, sampling 10% of the total number of clients per FL round. |