Minjun Kim, Minje Kim
We lifted 13 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| danny0628/HEART-PFL | canonical | 10 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| FedAveraging | Ran | danny0628/HEART-PFL/aggregation/averaging.py pointer only (licence: NOASSERTION) · get_code("0aaece534c92f75d") |
| adaad_inner_loss | Ran | danny0628/HEART-PFL/aggregation/AT_helper.py pointer only (licence: NOASSERTION) · get_code("f6a6a32c98350e2b") |
| computeAUROC | Ran | danny0628/HEART-PFL/aggregation/distillation.py pointer only (licence: NOASSERTION) · get_code("3975b1aa54ba551d") |
| conv1x1 | Ran | danny0628/HEART-PFL/models/resnet_bn_utils.py pointer only (licence: NONE) · get_code("2a80220dabcb742a") |
| conv3x3 | Ran | danny0628/HEART-PFL/models/resnet_bn_utils.py pointer only (licence: NOASSERTION) · get_code("54ef4a4d895678ee") |
| create_batch_norm | Ran | danny0628/HEART-PFL/models/resnet_bn_utils.py pointer only (licence: NOASSERTION) · get_code("b91186f3582c95f3") |
| divergence | Ran | danny0628/HEART-PFL/aggregation/distillation.py pointer only (licence: NOASSERTION) · get_code("5405f5e2e7253079") |
| flower102_noniid | Ran | danny0628/HEART-PFL/datasets/prepare_data.py pointer only (licence: NOASSERTION) · get_code("ae97cecf56042ffb") |
| mtad_inner_loss | Ran | danny0628/HEART-PFL/aggregation/AT_helper.py pointer only (licence: NOASSERTION) · get_code("67a43807986ec3bc") |
| test | Ran | danny0628/HEART-PFL/aggregation/distillation.py pointer only (licence: NOASSERTION) · get_code("6742d7a324b62056") |
| Madry_PGD | Not yet run | danny0628/HEART-PFL/aggregation/AT_helper.py pointer only (licence: NOASSERTION) · get_code("750f12ea8853901e") |
| cifar_noniid | Not yet run | danny0628/HEART-PFL/datasets/prepare_data.py pointer only (licence: NOASSERTION) · get_code("51c693c3a5f724fe") |
| get_dataset | Not yet run | danny0628/HEART-PFL/datasets/prepare_data.py pointer only (licence: NOASSERTION) · get_code("6fcb5d0981f1717c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Personalized Federated Learning (PFL) aims to deliver effective client-specific models under heterogeneous distributions, yet existing methods suffer from shallow prototype alignment and brittle server-side distillation. We propose HEART-PFL, a dual-sided framework that (i) performs depth-aware Hierarchical Directional Alignment (HDA) using cosine similarity in the early stage and MSE matching in the deep stage to preserve client specificity, and (ii) stabilizes global updates through Adversarial Knowledge Transfer (AKT) with symmetric KL distillation on clean and adversarial proxy data. Using lightweight adapters with only 1.46M trainable parameters, HEART-PFL achieves stateof-the-art personalized accuracy on CIFAR-100, Flowers-102, and Caltech-101 (63.42%, 84.23%, and 95.67%, respectively) under Dirichlet non-IID partitions, and remains robust to out-of-domain proxy data. Ablation studies further confirm that HDA and AKT provide complementary gains in alignment, robustness, and optimization stability, offering insights into how the two components mutually reinforce effective personalization. Overall, these results demonstrate that HEART-PFL simultaneously enhances personalization and global stability, highlighting its potential as a strong and scalable solution for PFL (code available at https://github.com/danny0628/HEART-PFL).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.24209")
get_code_for_paper("2603.24209")
have("2603.24209")
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