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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Authors: Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed p Fed Gate on four FL benchmarks compared to several SOTA methods. We show that p Fed Gate achieves superior global accuracy, individual accuracy and efficiency simultaneously (up to 4.53% average accuracy improvement with 12x smaller sparsity than the compared strongest PFL method). We demonstrate the effectiveness and robustness of p Fed Gate in the partial clients participation and novel clients participation scenarios. We find that p Fed Gate can learn meaningful sparse local models adapted to different data distributions, and conduct extensive experiments to study the effect of sparsity and verify the necessity and effectiveness of p Fed Gate components. |
| Researcher Affiliation | Industry | 1Alibaba Group. Correspondence to: Yaliang Li <EMAIL>. |
| Pseudocode | Yes | We summarize the overall algorithm in Algorithm 1. Besides, we present more details about (1) the gradients flow via the gating layer, which contains a knapsack solver; and (2) the global model aggregation. |
| Open Source Code | Yes | Our codes are at https://github.com/alibaba/FederatedScope/tree/master/benchmark/pFL-Bench. |
| Open Datasets | Yes | We adopt four widely used FL datasets in our experiments: EMNIST (Cohen et al., 2017), FEMNIST (Caldas et al., 2018), CIFAR10 and CIFAR100 (Krizhevsky, 2009). |
| Dataset Splits | Yes | All datasets are randomly split into train/valid/test sets with a ratio 6:2:2. |
| Hardware Specification | Yes | We implement all models with Py Torch, and run experiments on Tesla V100 and NVIDIA Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions 'We implement all models with Py Torch' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For each method on each dataset, we use the SGD optimizer and grid search the learning rate ηg from [0.005, 0.01, 0.03, 0.05, 0.1, 0.3, 0.5], set the communication round T = 400, the batch size as 128 and the local update step as 1 epoch. For p Fed Gate, the learning rate of gating layer η is searched from [0.01, 0.05, 0.1, 0.3, 0.5, 1, 1.5], and we set the block size splitting factor B = 5 for all evaluated models. |