We lifted 8 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| kasraborazjani/task-perspective-het | canonical | 7 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| compute_pck | Ran | kasraborazjani/task-perspective-het/calc_pck_keypoints3d.py code served (permissive licence) · get_code("38461541e0c3c23e") |
| get_chkpt_name | Ran | kasraborazjani/task-perspective-het/train_centralized_task_model_multidataset.py code served (permissive licence) · get_code("dcf3a3d8b3b21c39") |
| get_default_chkpt_name | Ran | kasraborazjani/task-perspective-het/extract_cluster_and_dirichlet_viz.py code served (permissive licence) · get_code("9e518d54b8b148f7") |
| get_run_dir | Ran | kasraborazjani/task-perspective-het/extract_cluster_and_dirichlet_viz.py code served (permissive licence) · get_code("11f8754fc2a611c1") |
| get_semseg_fname | Ran | kasraborazjani/task-perspective-het/make_pseudo_scene_labels_pascal.py code served (permissive licence) · get_code("07c2149680861df5") |
| heatmaps_to_coords | Ran | kasraborazjani/task-perspective-het/calc_pck_keypoints3d.py code served (permissive licence) · get_code("1c804d0a3b938905") |
| return_task_loss | Ran | kasraborazjani/task-perspective-het/losses.py code served (permissive licence) · get_code("26a10a0eea32335f") |
| return_task_loss | Not yet run | kasraborazjani/task-perspective-het/pascal_losses.py code served (permissive licence) · get_code("204faf9fe3e3fefb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Federated Learning (FL) has emerged as one of the prominent paradigms for distributed machine learning (ML). However, it is well-established that its performance can degrade significantly under non-IID (non-independent and identically distributed) data distributions across clients. To study this effect, the existing works predominantly emulate data heterogeneity by imposing label distribution skew across clients. In this paper, we show that label distribution skew fails to fully capture the data heterogeneity in computer vision tasks beyond classification, exposing an overlooked gap in the literature. Motivated by this, by utilizing pre-trained deep neural networks to extract task-specific data embeddings, we define task-specific data heterogeneity through the lens of each vision task and introduce a new level of data heterogeneity called embedding-based data heterogeneity. Our methodology involves clustering data points based on embeddings and distributing them among clients using the Dirichlet distribution. Through extensive experiments, we evaluate the performance of different FL methods under our revamped notion of data heterogeneity, introducing new benchmark performance measures to the literature. For instance, across seven representative computer vision tasks, our embedding-based heterogeneity formulation leads to up to around 60% increase in the observed loss under FedAvg, indicating that it more accurately exposes the performance degradation caused by data heterogeneity. We further unveil a series of open research directions that can be pursued. (Code: https://github.com/KasraBorazjani/task-perspective-het.git)
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.14553")
get_code_for_paper("2503.14553")
have("2503.14553")
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