A benchmark for proximity-based non-IID Federated Learning
Project description
ProFed: A Benchmark for Proximity-based Federated Learning
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
profed-0.5.0.tar.gz
(4.0 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file profed-0.5.0.tar.gz.
File metadata
- Download URL: profed-0.5.0.tar.gz
- Upload date:
- Size: 4.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.8.3 CPython/3.12.10 Linux/6.11.0-1014-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
91b913d8dddcf23b967834fc35dbcf513172138e0da6f7696ca43f236c1d371a
|
|
| MD5 |
b5b43a957a6ca78ed587344e71a96a10
|
|
| BLAKE2b-256 |
2285a587c49a1e17cb37425c0f62a2d78efec554be2b266798d7fc50575fd54a
|
File details
Details for the file profed-0.5.0-py3-none-any.whl.
File metadata
- Download URL: profed-0.5.0-py3-none-any.whl
- Upload date:
- Size: 5.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.8.3 CPython/3.12.10 Linux/6.11.0-1014-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
05160da4e9d9a08f6fab4dd281e3e8313e82eefb98e33a0be78255eafa0694bb
|
|
| MD5 |
74d73572fa8332ee3337efd97a59b3d4
|
|
| BLAKE2b-256 |
38cb1f4e062fe90f7667e29627c134701ce950af6efbeb1885e9af67d0642585
|
