Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
Authors: Xueyang Tang, Song Guo, Jie ZHANG, Jingcai Guo
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on diverse datasets validate the superiority of Fed SDR over the state-of-the-art PFL methods on OOD generalization performance. |
| Researcher Affiliation | Academia | Xueyang Tang1, Song Guo2 , Jie Zhang1 & Jingcai Guo1 1The Hong Kong Polytechnic University 2The Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 Fed SDR: Federated Learning with Shortcut Discovery and Removal |
| Open Source Code | Yes | Code is available at https://github.com/Tangx-yy/Fed SDR. |
| Open Datasets | Yes | Colored-MNIST (CMNIST) (Arjovsky et al., 2019) is constructed based on MNIST (Le Cun et al., 1998), Colored Fashion-MNIST (CFMNIST) (Ahuja et al., 2020), Water Bird (Sagawa et al., 2019), PACS (Li et al., 2017). |
| Dataset Splits | Yes | The hyper-parameters of the competitors and our algorithm are tuned to make the accuracy on the validation environment (i.e., pe val = 0.10) as high as possible. and We adopt the leave-one-domain-out strategy to evaluate the OOD generalization performance. |
| Hardware Specification | Yes | We simulate a set of clients and a centralized server on one deep learning workstation (Intel(R) Core(TM) i9-12900K CPU @ 3.20GHz with one NVIDIA Ge Force RTX 3090 GPU). |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The hyper-parameters of the competitors and our algorithm are tuned to make the accuracy on the validation environment (i.e., pe val = 0.10) as high as possible. Specifically, the mainly used hyperparameters in the evaluation part are listed as follows: Global communication round: T = 600, Local iterations: R = 10, Personalized epochs to update the personalized invariant predictors: K = 10, Local batch size: B = 50, Global learning rate: β = 0.0001, Personalized learning rate: η = 0.0001, Discrepancy threshold: α = 1.0, Balancing weight: λ = 0.5, Balancing weight: γ = 1.4, Optimizer: Adam. |