Systems and methods for providing data privacy using federated learning

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.

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.