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# Licensed under the Apache License, Version 2.0 (the "License");
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# ==============================================================================
"""Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent.
[Blanchard et al., 2017].
Paper: proceedings.neurips.cc/paper/2017/file/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf
"""
from logging import INFO
from typing import Callable, Optional
from flwr.common import MetricRecord, RecordDict, log
from .multikrum import MultiKrum
# pylint: disable=too-many-instance-attributes
[docs]
class Krum(MultiKrum):
"""Krum [Blanchard et al., 2017] strategy.
Implementation based on https://arxiv.org/abs/1703.02757
Parameters
----------
fraction_train : float (default: 1.0)
Fraction of nodes used during training. In case `min_train_nodes`
is larger than `fraction_train * total_connected_nodes`, `min_train_nodes`
will still be sampled.
fraction_evaluate : float (default: 1.0)
Fraction of nodes used during validation. In case `min_evaluate_nodes`
is larger than `fraction_evaluate * total_connected_nodes`,
`min_evaluate_nodes` will still be sampled.
min_train_nodes : int (default: 2)
Minimum number of nodes used during training.
min_evaluate_nodes : int (default: 2)
Minimum number of nodes used during validation.
min_available_nodes : int (default: 2)
Minimum number of total nodes in the system.
num_malicious_nodes : int (default: 0)
Number of malicious nodes in the system. Defaults to 0.
weighted_by_key : str (default: "num-examples")
The key within each MetricRecord whose value is used as the weight when
computing weighted averages for MetricRecords.
arrayrecord_key : str (default: "arrays")
Key used to store the ArrayRecord when constructing Messages.
configrecord_key : str (default: "config")
Key used to store the ConfigRecord when constructing Messages.
train_metrics_aggr_fn : Optional[callable] (default: None)
Function with signature (list[RecordDict], str) -> MetricRecord,
used to aggregate MetricRecords from training round replies.
If `None`, defaults to `aggregate_metricrecords`, which performs a weighted
average using the provided weight factor key.
evaluate_metrics_aggr_fn : Optional[callable] (default: None)
Function with signature (list[RecordDict], str) -> MetricRecord,
used to aggregate MetricRecords from training round replies.
If `None`, defaults to `aggregate_metricrecords`, which performs a weighted
average using the provided weight factor key.
"""
# pylint: disable=too-many-arguments,too-many-positional-arguments
def __init__(
self,
fraction_train: float = 1.0,
fraction_evaluate: float = 1.0,
min_train_nodes: int = 2,
min_evaluate_nodes: int = 2,
min_available_nodes: int = 2,
num_malicious_nodes: int = 0,
weighted_by_key: str = "num-examples",
arrayrecord_key: str = "arrays",
configrecord_key: str = "config",
train_metrics_aggr_fn: Optional[
Callable[[list[RecordDict], str], MetricRecord]
] = None,
evaluate_metrics_aggr_fn: Optional[
Callable[[list[RecordDict], str], MetricRecord]
] = None,
) -> None:
super().__init__(
fraction_train=fraction_train,
fraction_evaluate=fraction_evaluate,
min_train_nodes=min_train_nodes,
min_evaluate_nodes=min_evaluate_nodes,
min_available_nodes=min_available_nodes,
weighted_by_key=weighted_by_key,
num_malicious_nodes=num_malicious_nodes,
num_nodes_to_select=1, # Krum selects 1 node
arrayrecord_key=arrayrecord_key,
configrecord_key=configrecord_key,
train_metrics_aggr_fn=train_metrics_aggr_fn,
evaluate_metrics_aggr_fn=evaluate_metrics_aggr_fn,
)
[docs]
def summary(self) -> None:
"""Log summary configuration of the strategy."""
log(INFO, "\t├──> Krum settings:")
log(INFO, "\t│\t└── Number of malicious nodes: %d", self.num_malicious_nodes)
super().summary()