Enable Fairness-Aware Federated Learning

This Merge Request extends the capabilities of DecLearn, bringing Fairness to the party.

It is the result of a year-long collaboration with Michaël Perrot and Brahim Erraji to design and evaluate algorithms to learn models under fairness constraints in a federated learning setting, using either newly-introduced algorithms or existing ones from the litterature. This collaboration first produced a legacy fairgrad branch that was used to conduct preliminary experiments resulting in a publication at the CAp 2024 conference. The current MR is the result of additional efforts to clean up, improve and robustify the resulting code, introducing a shared and extensible API abstracted from the algorithms at hand, adding support for secure aggregation, and submitting the code to extensive unit and functional tests.

Overall, this MR:

• Adds a new declearn.fairness submodule, that defines an API and concrete algorithms for fairness-aware federated learning.
  • The API is dedicated to group-fairness, enforced by altering the model training process.
  • Currently, 4 distinct fairness definitions are supported (Accuracy Parity; Demographic Parity; Equality of Opportunity; Equalized Odds).
  • Currently, 3 distinct algorithms are implemented (Fed-FairGrad; Fed-FairBatch/FedFB; FairFed), plus a monitoring mode to measure fairness of baseline federated learning.
  • The API and all current algorithms support using SecAgg to protect exchanged fairness-related quantities.
• Integrates fairness algorithms as part of the declearn.main orchestration and configuration tools.
  • declearn.main.config.FLOptimConfig now exposes a "fairness" field for using a fairness algorithm as part of a federated optimization strategy.
  • declearn.main.config.FLRunConfig now exposes a "fairness" field to specify some hyper-parameters, which are otherwise automatically and dynamically filled.
  • Both FederatedServer and FederatedClient have new methods that implement routines related to fairness evaluation and enforcement.
• Adds a new declearn.training submodule, that relocates the former declearn.main.training.
• Adds extensive documentation and tests about the newly-introduced fairness-related features.

Overall to-do list:

• Design and implement a shared API for fairness-aware federated learning.
• Integrate fairness-aware federated learning to declearn.main.
• (Re-)implement Fed-FairGrad, Fed-FairBatch and FairFed, adding SecAgg support.
• Add unit tests for the introduced API and concrete algorithms.
• Add functional tests for the algorithms over a toy dataset.
• Write an extensive guide towards end-users.
• Finalize the implementation up to the last hanging details.

Remaining tasks:

• Change the way FairnessControllerServer redefines the used Aggregator?
• Add parsing options for server-side fairness controller choice.
• Add parsing options for client-side fairness dataset specification.

Future tasks:

• Modularize fairness rounds' frequency (and evaluation ones').
• Enable delaying fairness setup (run some rounds without fairness constraints).