US 2025/0232188 A1Published application

Method and system for a cross-silo serverless collaborative learning in a malicious client threat-model

AssigneeTata Consultancy Services Limited

InventorsDELTON MYALIL ANTONY, RAJAN MINDIGAL ALASINGARA BHATTACHAR, IMTIYAZUDDIN SHAIK, SACHIN PREMSUKH LODHA

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Abstract

Traditional deep learning techniques are performed by high-performance system with direct access to the data to train large models. An approach of training the model from a collaboration of similar stakeholders where they pool together their data in a central server. However, data privacy is lost by exposing said models and data security while accessing heterogeneous data. Embodiments of the present disclosure provide a method and system for a cross-silo serverless collaborative learning among a plurality of clients in a malicious client threat-model based on a decentralized Epsilon cluster selection. Protocols are initialized and considered to iteratively train local models associated with each client and aggregate the local models as private input based on the multi-party computation to obtain global model. Non-linear transformation of a silhouette score to an Epsilon probability without implementing a server to select rth model from an active set to assign as the global model.

Claims18

Dependent claims

A processor implemented method, comprising:

initializing, via one or more hardware processors, one or more protocols corresponding to a plurality of clients to obtain one or more public information;
and
iteratively performing, via the one or more hardware processors, based on the one or more protocols, comprises:
training, via the one or more hardware processors, at least one local model associated with each client, wherein each local model corresponds to a copy of a global model, and wherein the one or more local models correspond to at least one parametric model ranging from a logistic regression to one or more large neural network with one or more trainable parameters;
and
aggregating, via the one or more hardware processors, the one or more local models based on a multi-party computation (MPC) to obtain one or more global models, and wherein the plurality of clients consider corresponding one or more local models as a private input to a function to obtain the one or more global models, and wherein the aggregation of the one or more local models, comprises:
i) transforming, via the one or more hardware processors, at least one local model from the one or more local models corresponding to each client to one dimensional vector locally, wherein one or more individual layers of weights and biases are extracted and concatenated to generate a long sequence;
ii) reducing, via the one or more hardware processors, one dimensional vector of each local model by a MPC based dimensionality reduction technique to obtain one or more dimension-reduced model vectors, and wherein a number of principal components obtained are same as a number of clients in participation;
iii) creating, via the one or more hardware processors, at least one local share of the one or more dimension-reduced model vectors and distributed across the plurality of clients based on the MPC, and wherein each client receives a portion of data associated with other clients;
iv) determining, via the one or more hardware processors, a silhouette score based on the MPC by clustering the one or more dimension-reduced model vectors into at least one of:
(a) a minority cluster, or (b) a majority cluster;
v) transforming, via the one or more hardware processors, the silhouette score into an epsilon probability (E);
vi) selecting, via the one or more hardware processors, at least one of (a) a largest cluster, or (b) an entire set of models as an active set, based on the epsilon probability (E);
and
vii) selecting, via the one or more hardware processors, a rth model from the active set to assign as the global model.

12 more elements

A system, comprising:

a memory storing instructions;
one or more communication interfaces;
and
one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
initialize one or more protocols corresponding to a plurality of clients to obtain one or more public information;
iteratively perform based on the one or more protocols, comprises:
train at least one local model associated with each client, wherein each local model corresponds to a copy of a global model, and wherein the one or more local models correspond to at least one parametric model ranging from a logistic regression to one or more large neural network with one or more trainable parameters;
and
aggregate the one or more local models based on a multi-party computation (MPC) to obtain one or more global models, and wherein the plurality of clients consider corresponding one or more local models as a private input to a function to obtain the one or more global models, and wherein the aggregation of the one or more local models, comprises:
i) transform at least one local model from the one or more local models corresponding to each client to one dimensional vector locally, wherein one or more individual layers of weights and biases are extracted and concatenated to generate a long sequence;
ii) reduce one dimensional vector of each local model by a MPC based dimensionality reduction technique to obtain one or more dimension-reduced model vectors, and wherein a number of principal components obtained are same as a number of clients in participation;
iii) create at least one local share of the one or more dimension-reduced model vectors and distributed across the plurality of clients based on the MPC, and wherein each client receives a portion of data associated with other clients;
iv) determine a silhouette score based on the MPC by clustering the one or more dimension-reduced model vectors into at least one of:
(a) a minority cluster, or (b) a majority cluster;
v) transform the silhouette score into an epsilon probability (E);
vi) select at least one of (a) a largest cluster, or (b) an entire set of models as an active set, based on the epsilon probability (E);
and
vii) select a rth model from the active set to assign as the global model.

15 more elements

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

initializing one or more protocols corresponding to a plurality of clients to obtain one or more public information;
and
iteratively performing based on the one or more protocols, comprises:
training at least one local model associated with each client, wherein each local model corresponds to a copy of a global model, and wherein the one or more local models correspond to at least one parametric model ranging from a logistic regression to one or more large neural network with one or more trainable parameters;
and
aggregating the one or more local models based on a multi-party computation (MPC) to obtain one or more global models, and wherein the plurality of clients consider corresponding one or more local models as a private input to a function to obtain the one or more global models, and wherein the aggregation of the one or more local models, comprises:
i) transforming at least one local model from the one or more local models corresponding to each client to one dimensional vector locally, wherein one or more individual layers of weights and biases are extracted and concatenated to generate a long sequence;
ii) reducing one dimensional vector of each local model by a MPC based dimensionality reduction technique to obtain one or more dimension-reduced model vectors, and wherein a number of principal components obtained are same as a number of clients in participation;
iii) creating at least one local share of the one or more dimension-reduced model vectors and distributed across the plurality of clients based on the MPC, and wherein each client receives a portion of data associated with other clients;
iv) determining a silhouette score based on the MPC by clustering the one or more dimension-reduced model vectors into at least one of:
(a) a minority cluster, or (b) a majority cluster;
v) transforming the silhouette score into an epsilon probability (E);
vi) selecting at least one of (a) a largest cluster, or (b) an entire set of models as an active set, based on the epsilon probability (E);
and
vii) selecting a rth model from the active set to assign as the global model.

12 more elements

Dependencies are inferred from the claim text.