Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Youssef Allouah, Anne-Marie Kermarrec, Rafael Pires, Rishi Sharma, Akash Dhasade
We lifted 10 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| sacs-epfl/fens | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| Meter | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("5208240b1cd6c19b") |
| _evaluate | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("93f4d96f2278b93b") |
| _one_hot | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("d72787a1daa64a40") |
| _train_and_evaluate | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("2ca89b3fc6a05a88") |
| comp_accuracy | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("08b050ad57b64351") |
| evaluate_competencies_v2 | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("05787e35341c7132") |
| get_num_classes | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("2509af88160cc0c7") |
| get_prediction_using_competency | Ran | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("8b6e33bdae2b5151") |
| _check_accuracy | Not yet run | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("aafeb8a55133cbcc") |
| evaluate_all_aggregations | Not yet run | sacs-epfl/fens/aggregations.py pointer only (licence: NONE) · get_code("6dea375bb6e99c59") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iterative exchange of models between clients and the server with a single round of communication, thereby saving substantially on communication costs. Not surprisingly, OFL exhibits a performance gap in terms of accuracy with respect to FL, especially under high data heterogeneity. We introduce FENS, a novel federated ensembling scheme that approaches the accuracy of FL with the communication efficiency of OFL. Learning in FENS proceeds in two phases: first, clients train models locally and send them to the server, similar to OFL; second, clients collaboratively train a lightweight prediction aggregator model using FL. We showcase the effectiveness of FENS through exhaustive experiments spanning several datasets and heterogeneity levels. In the particular case of heterogeneously distributed CIFAR-10 dataset, FENS achieves up to a 26.9% higher accuracy over state-of-the-art (SOTA) OFL, being only 3.1% lower than FL. At the same time, FENS incurs at most 4.3× more communication than OFL, whereas FL is at least 10.9× more communication-intensive than FENS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.07182")
get_code_for_paper("2411.07182")
have("2411.07182")
Connect an agent — have() is free.