Feature federated learning

[kdt523]

This PR adds comprehensive doctests to [federated_averaging.py] and adjusts expected float literals to match Python’s default float representation. The goal is to ensure the module is verified by CI via doctests and to avoid false negatives due to minor float formatting differences.
What’s included:
Doctests covering:
Equal-weight aggregation across multiple tensors (vector and 2x2 matrix)
Weighted aggregation with custom non-negative weights
Error cases: no clients, mismatched number of tensors, mismatched shapes, invalid weights (negative, zero-sum, wrong shape)
A concise docstring for [federated_average] (parameters and returns)
No functional changes to the algorithm—behavior remains the same
Scope:
File changed: [federated_averaging.py]
No new files, no external dependencies added
Reference:
Federated learning overview: [https://en.wikipedia.org/wiki/Federated_learning]
Tests:
Doctests run locally: 16 tests, 16 passed, 0 failed (module-only doctest run)
Fixes #13612

[algorithms-keeper]

Closing this pull request as invalid

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