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@article{209947,
        author = {VARSHINI G and DINESH KUMAR P},
        title = {A Survey of Trust-Enhanced Blockchain-Assisted Federated Learning for Secure Collaboration in Vehicular Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3926-3932},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209947},
        abstract = {Connected and autonomous vehicles generate large volumes of sensor, trajectory and driving-behaviour data, and federated learning (FL) allows them to build shared models without uploading raw data to a central server. Open vehicular environments, however, combine high mobility, unstable links and participants of unknown reliability, which exposes FL to poisoned updates, free-riding, a vulnerable central aggregator and contributions that nobody can verify. Blockchain supplies tamper-evident records and decentralised coordination, while trust and reputation models help decide which vehicles deserve influence over the global model. This paper surveys research that combines these three ingredients. We summarise the background, classify existing schemes by trust evaluation method, ledger design and aggregation strategy, compare representative proposals, and map common attacks to the defences that trust-enhanced architectures offer. We then discuss open problems, namely consensus latency under mobility, on-chain storage, privacy leakage from transparent ledgers, Sybil and collusion attacks on reputation, and the absence of shared benchmarks, and we outline directions toward lightweight, adaptive and standardised designs for secure vehicular collaboration.},
        keywords = {Federated Learning, Blockchain, Trust Management, Reputation, Vehicular Networks, Internet of Vehicles, Secure Collaboration, Poisoning Attacks.},
        month = {October},
        }