nvflare.app_common.filters package¶
Submodules¶
- nvflare.app_common.filters.convert_weights module
- nvflare.app_common.filters.dxo_blocker module
- nvflare.app_common.filters.exclude_vars module
- nvflare.app_common.filters.percentile_privacy module
- nvflare.app_common.filters.statistics_privacy_filter module
- nvflare.app_common.filters.svt_privacy module
Module contents¶
- class ExcludeVars(exclude_vars: List[str] | str | None = None, data_kinds: List[str] | None = None)[source]¶
Bases:
DXOFilter
Exclude/Remove variables from Shareable.
- Parameters:
exclude_vars (Union[List[str], str, None] , optional) – variables/layer names to be excluded.
data_kinds – kinds of DXO object to filter
Notes
- Based on different types of exclude_vars, this filter has different behavior:
if a list of variable/layer names, only specified variables will be excluded. if a string, it will be converted into a regular expression, only matched variables will be excluded. if not provided or other formats the Shareable remains unchanged.
- process_dxo(dxo: DXO, shareable: Shareable, fl_ctx: FLContext) None | DXO [source]¶
Called by upper layer to remove variables in weights/weight_diff dictionary.
When the return code of shareable is not ReturnCode.OK, this function will not perform any process and returns the shareable back.
- Parameters:
dxo (DXO) – DXO to be filtered.
shareable – that the dxo belongs to
fl_ctx (FLContext) – only used for logging.
Returns: filtered dxo
- class PercentilePrivacy(percentile=10, gamma=0.01, data_kinds: List[str] | None = None)[source]¶
Bases:
DXOFilter
Implementation of “largest percentile to share” privacy preserving policy.
Shokri and Shmatikov, Privacy-preserving deep learning, CCS ‘15
- Parameters:
percentile (int, optional) – Only abs diff greater than this percentile is updated. Allowed range 0..100. Defaults to 10.
gamma (float, optional) – The upper limit to truncate abs values of weight diff. Defaults to 0.01. Any weight diff with abs<gamma will become 0.
data_kinds – kinds of DXO to filter
- process_dxo(dxo: DXO, shareable: Shareable, fl_ctx: FLContext) None | DXO [source]¶
Compute the percentile on the abs delta_W.
Only share the params where absolute delta_W greater than the percentile value
- Parameters:
dxo – information from client
shareable – that the dxo belongs to
fl_ctx – context provided by workflow
Returns: filtered dxo
- class SVTPrivacy(fraction=0.1, epsilon=0.1, noise_var=0.1, gamma=1e-05, tau=1e-06, data_kinds: [<class 'str'>] = None, replace=True)[source]¶
Bases:
DXOFilter
Implementation of the standard Sparse Vector Technique (SVT) differential privacy algorithm.
lambda_rho = gamma * 2.0 / epsilon threshold = tau + np.random.laplace(scale=lambda_rho)
- Parameters:
fraction (float, optional) – used to determine dataset threshold. Defaults to 0.1.
epsilon (float, optional) – Defaults to 0.1.
noise_var (float, optional) – additive noise. Defaults to 0.1.
gamma (float, optional) – Defaults to 1e-5.
tau (float, optional) – Defaults to 1e-6.
data_kinds (str, optional) – Defaults to None.
replace (bool) – whether to sample with replacement. Defaults to True.