p2pfl.learning.aggregators.fedopt package

FedOpt family of aggregators for P2PFL.

This package provides implementations of the FedOpt family of federated optimization algorithms. See individual modules for detailed documentation.

class p2pfl.learning.aggregators.fedopt.FedAdagrad(eta=0.1, beta_1=0.9, tau=1e-09, disable_partial_aggregation=False)[source]

Bases: FedOptBase

FedAdagrad - Adaptive Federated Optimization using Adagrad [Reddi et al., 2020].

FedAdagrad adapts the Adagrad optimizer to federated settings, maintaining adaptive learning rates on the server side based on accumulated squared gradients.

Paper: https://arxiv.org/abs/2003.00295

Parameters:

  • eta (float)

  • beta_1 (float)

  • tau (float)

  • disable_partial_aggregation (bool)

class p2pfl.learning.aggregators.fedopt.FedAdam(eta=0.1, beta_1=0.9, beta_2=0.99, tau=1e-09, disable_partial_aggregation=False)[source]

Bases: FedOptBase

FedAdam - Adaptive Federated Optimization using Adam [Reddi et al., 2020].

FedAdam adapts the Adam optimizer to federated settings, maintaining both momentum and adaptive learning rates on the server side.

Paper: https://arxiv.org/abs/2003.00295

Parameters:

  • eta (float)

  • beta_1 (float)

  • beta_2 (float)

  • tau (float)

  • disable_partial_aggregation (bool)

class p2pfl.learning.aggregators.fedopt.FedOptBase(eta=0.1, beta_1=0.9, tau=1e-09, disable_partial_aggregation=False)[source]

Bases: FedAvg

Base class for Federated Optimization (FedOpt) family [Reddi et al., 2020].

This class extends FedAvg to provide common functionality for adaptive federated optimization algorithms like FedAdagrad, FedAdam, and FedYogi.

Paper: https://arxiv.org/abs/2003.00295

Parameters:

  • eta (float)

  • beta_1 (float)

  • tau (float)

  • disable_partial_aggregation (bool)

SUPPORTS_PARTIAL_AGGREGATION: bool = False
addr: str
aggregate(models)[source]

Aggregate models using FedOpt algorithm.

Parameters:

models (list[P2PFLModel]) – List of P2PFLModel objects to aggregate.

Return type:

P2PFLModel

Returns:

A P2PFLModel with the optimized parameters.

current_weights: list[ndarray]
m_t: list[ndarray]
partial_aggregation: bool
class p2pfl.learning.aggregators.fedopt.FedYogi(eta=0.01, beta_1=0.9, beta_2=0.99, tau=0.001, disable_partial_aggregation=False)[source]

Bases: FedOptBase

FedYogi - Adaptive Federated Optimization using Yogi [Reddi et al., 2020].

FedYogi adapts the Yogi optimizer to federated settings, maintaining adaptive learning rates on the server side to handle heterogeneous data distributions.

Paper: https://arxiv.org/abs/2003.00295

Parameters:

  • eta (float)

  • beta_1 (float)

  • beta_2 (float)

  • tau (float)

  • disable_partial_aggregation (bool)

Submodules