DisT-FL: Enhancing Security for TEE-Based Aggregation in Federated Learning

Federated learning (a machine learning approach where multiple computers train a model together without sharing raw data) systems using TEEs (trusted execution environments, secure areas within processors that protect data even from the main operating system) can be attacked by malicious servers that exploit TEE limitations like state rollback (reverting to previous states) and I/O manipulation (tampering with input/output). This paper presents DisT-FL, a solution using multiple TEEs working together in an append-only ledger (an unchangeable record of transactions) to secure federated learning aggregation (combining results from all participants) and prevent these attacks.