  Federated Learning is susceptible to various kinds of attacks like Data
Poisoning, Model Poisoning and Man in the Middle attack. We perceive Federated
Learning as a hierarchical structure, a federation of nodes with validators as
the head. The process of validation is done through consensus by employing
Novelty Detection and Snowball protocol, to identify valuable and relevant
updates while filtering out potentially malicious or irrelevant updates, thus
preventing Model Poisoning attacks. The opinion of the validators is recorded
in blockchain and trust score is calculated. In case of lack of consensus,
trust score is used to determine the impact of validators on the global model.
A hyperparameter is introduced to guide the model generation process, either to
rely on consensus or on trust score. This approach ensures transparency and
reliability in the aggregation process and allows the global model to benefit
from insights of most trusted nodes. In the training phase, the combination of
IPFS , PGP encryption provides : a) secure and decentralized storage b)
mitigates single point of failure making this system reliable and c) resilient
against man in the middle attack. The system is realized by implementing in
python and Foundry for smart contract development. Global Model is tested
against data poisoning by flipping the labels and by introducing malicious
nodes. Results found to be similar to that of Flower.
