  Federated Learning (FL) presents a promising paradigm for training machine
learning models across decentralized edge devices while preserving data
privacy. Ensuring the integrity and traceability of data across these
distributed environments, however, remains a critical challenge. The ability to
create transparent artificial intelligence, such as detailing the training
process of a machine learning model, has become an increasingly prominent
concern due to the large number of sensitive (hyper)parameters it utilizes;
thus, it is imperative to strike a reasonable balance between openness and the
need to protect sensitive information.
  In this paper, we propose one of the first approaches to enhance data
provenance and model transparency in federated learning systems. Our
methodology leverages a combination of cryptographic techniques and efficient
model management to track the transformation of data throughout the FL process,
and seeks to increase the reproducibility and trustworthiness of a trained FL
model. We demonstrate the effectiveness of our approach through experimental
evaluations on diverse FL scenarios, showcasing its ability to tackle
accountability and explainability across the board.
  Our findings show that our system can greatly enhance data transparency in
various FL environments by storing chained cryptographic hashes and client
model snapshots in our proposed design for data decoupled FL. This is made
possible by also employing multiple optimization techniques which enables
comprehensive data provenance without imposing substantial computational loads.
Extensive experimental results suggest that integrating a database subsystem
into federated learning systems can improve data provenance in an efficient
manner, encouraging secure FL adoption in privacy-sensitive applications and
paving the way for future advancements in FL transparency and security
features.
