SYSTEMS AND METHODS FOR PROVIDING DATA PRIVACY USING FEDERATED LEARNING
Data privacy using federated learning is provided by receiving one or more instance(s) of a master RL agent model and training the instance(s) of the master RL agent model on a corresponding graph, thereby generating corresponding sets of RL model weights. One or more information gains corresponding to one or more software stacks are generated. The information gain(s) and the RL model weight(s) are transmitted to a central server to enable the central server to update the master RL agent model based on the information gain(s) and the RL model weights.
1 . A method for providing data privacy using federated learning, comprising:
receiving a first instance of a master RL agent model;
training the first instance of the master RL agent model on a first graph, thereby generating a first set of RL model weights;
generating a first information gain corresponding to a first software stack of a first client; and
transmitting the first information gain and the first set of RL model weights to a central server to enable the central server to update the master RL agent model based on the first information gain and the first set of RL model weights.
2 . The method according to claim 1 , further comprising:
updating the master RL agent model based on the first information gain and the first set of RL model weights, thereby generating an updated master RL agent model.
3 . The method according to claim 1 , further comprising:
receiving an updated master RL agent model; and
training the updated master RL agent model on another graph.
4 . The method of claim 1 , further comprising:
receiving a second instance of the master RL agent model;
training the second instance of the master RL agent model on a second graph, thereby generating a second set of RL model weights;
generating a second information gain corresponding to a second software stack of a second client; and
transmitting the second information gain and the second RL model weights to the central server to enable the central server to update the master RL agent model based on the first information gain, the first set of RL model weights, the second information gain and the second set of RL model weights.
5 . The method according to claim 4 , further comprising:
updating the master RL agent model based on the first information gain, the first set of RL model weights, the second information gain and the second set of RL model weights, thereby generating an updated master RL agent model.
6 . (canceled)
7 . The method of claim 4 , further comprising:
combining the first set of RL model weights and the second set of RL model weights to generate a combined set of RL model weights.
8 . The method according to claim 7 , further comprising:
updating the master RL agent model by applying the combined set of RL model weights.
9 . The method according to claim 4 , further comprising:
updating the master RL agent model by applying a weighted average based on the first information gain and the second information gain.
10 . A non-transitory computer-readable medium having stored thereon sequences of instructions, the sequences of instructions including instructions which when executed by a computer system causes the computer system to perform:
receiving a first instance of a master RL agent model;
training the first instance of the master RL agent model on a first graph, thereby generating a first set of RL model weights;
generating a first information gain corresponding to a first software stack of a first client; and
transmitting the first information gain and the first set of RL model weights to a central server to enable the central server to update the master RL agent model based on the first information gain and the first set of RL model weights.
11 . The non-transitory computer-readable medium of claim 10 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
updating the master RL agent model based on the first information gain and the first set of RL model weights, thereby generating an updated master RL agent model.
12 . The non-transitory computer-readable medium of claim 10 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
receiving an updated master RL agent model; and
training the updated master RL agent model on another graph.
13 . The non-transitory computer-readable medium of claim 10 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
receiving a second instance of the master RL agent model;
training the second instance of the master RL agent model on a second graph, thereby generating a second set of RL model weights;
generating a second information gain corresponding to a second software stack of a second client; and
transmitting the second information gain and the second RL model weights to the central server to enable the central server to update the master RL agent model based on the first information gain, the first set of RL model weights, the second information gain and the second set of RL model weights.
14 . The non-transitory computer-readable medium of claim 13 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
updating the master RL agent model based on the first information gain, the first set of RL model weights, the second information gain and the second set of RL model weights, thereby generating an updated master RL agent model.
15 . (canceled)
16 . The non-transitory computer-readable medium of claim 13 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
combining the first set of RL model weights and the second set of RL model weights to generate a combined set of RL model weights.
17 . The non-transitory computer-readable medium of claim 16 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
updating the master RL agent model by applying the combined set of RL model weights.
18 . The non-transitory computer-readable medium of claim 13 , further having stored thereon a sequence of instructions for causing the one or more processors to perform:
updating the master RL agent model by applying a weighted average based on the first information gain and the second information gain.
19 . A federated learning data privacy system, comprising:
a first agent server 104 - 1 having a first neural network configured to:
receive a first instance of a master RL agent model, and
train, by the first neural network the first instance of the master RL agent model on one of a plurality of graphs, thereby generating a first set of RL model weights; and
a first information gain calculator communicatively coupled to the first agent server and to a central server, configured to:
generate a first information gain corresponding to a first software stack of a first client, and
transmit the first information gain and the first set of RL model weights to the central server to enable the central server to update the master RL agent model based on the first information gain and the first set of RL model weights.
20 . The system according to claim 19 , wherein the central server is configured to update the master RL agent model based on the first information gain and the first set of RL model weights, thereby generating an updated master RL agent model.
21 . The system according to claim 19 , wherein:
the first agent server is configured to receive an updated master RL agent model; and
the first neural network is configured to train the updated master RL agent model on another one of the plurality of graphs.
22 . The system of claim 19 , further comprising:
a second agent server having a second neural network, configured to:
receive a second instance of the master RL agent model, and
train the second instance of the master RL agent model on a second graph, thereby generating a second set of RL model weights; and
a second information gain calculator further configured to:
generate a second information gain corresponding to a second software stack of a second client, and
transmit the second information gain and the second RL model weights to the central server to enable the central server to update the master RL agent model based on the first information gain, the first set of RL model weights, the second information gain and the second set of RL model weights.
23 . The system according to claim 22 , wherein the central server is further configured to update the master RL agent model based on the first information gain, the first set of RL model weights, the second information gain and the second set of RL model weights, thereby generating an updated master RL agent model.
24 . (canceled)
25 . The system of claim 22 , further comprising:
a weighted average calculator configured to combine the first set of RL model weights and the second set of RL model weights to generate a combined set of RL model weights.
26 . The system according to claim 25 , wherein the weighted average calculator is configured to update the master RL agent model by applying the combined set of RL model weights.
27 . The system according to claim 22 , further comprising:
a weighted average calculator configured to update the master RL agent model by applying a weighted average based on the first information gain and the second information gain.
28 . The system according to claim 22 , wherein the first agent server and the second agent server are on different systems.
29 . The system according to claim 22 , wherein the first agent server and the second agent server are on the same system.
30 - 53 . (canceled)