With the development of decentralized artificial intelligence (AI) networks, federated learning (FL) has received extensive attention for its ability to enable collaborative modeling without sharing raw data. However, existing methods are prone to convergence instability under non-independent and identically distributed (non-IID) conditions, lack robustness in adversarial settings, and have not yet sufficiently addressed fairness and incentive issues in multi-source heterogeneous environments. This paper proposes a Symmetry-Preserving Federated Learning (SPFL) framework that integrates blockchain auditing and fairness-aware incentive mechanisms. At the optimization layer, the framework employs group-theoretic regularization to maintain parameter symmetry and mitigate gradient conflicts; at the system layer, it leverages blockchain ledgers and smart contracts to verify and trace client updates; and at the incentive layer, it allocates rewards based on approximate Shapley values to ensure that the contributions of weaker clients are recognized. Experiments conducted on four datasets, MIMIC-IV ECG, AG News-Large, FEMNIST + Sketch, and IoT-SensorStream, show that SPFL improves average accuracy by about 7.7% compared to FedAvg, increases Jain’s Fairness Index by 0.05–0.06 compared to FairFed, and still maintains around 80% performance in the presence of 30% Byzantine clients. Convergence experiments further demonstrate that SPFL reduces the number of required rounds by about 30% compared to FedProx and exhibits lower performance degradation under high-noise conditions. These results confirm SPFL’s improvements in fairness and robustness, highlighting its application value in multi-source heterogeneous scenarios such as medical diagnosis, financial risk management, and IoT sensing.

Emerging tech entrepreneurs and venture capitalists face persistent challenges in quantifying enterprise worth, particularly when dealing with innovative business models. For entrepreneurs and investors, the necessity to expand financial resources and gauge the potential earnings of these firms heightens the significance of their evaluation. This paper presents an all-encompassing framework to evaluate the advantages and disadvantages of the current valuation approaches. Asset evaluation methods can be applied when a company has foreseeable future cash flows or when there is a definite degree of similarity among firms within the industry. However, new technology-based firms often struggle to meet this requirement. While financial management-based methods focus on financial indicators and risk assessment and ignores the important impact of non-financial factors such as the entrepreneurial team, technological innovation ability, and market competitive advantage on the firm’s value. Whereas entrepreneurial management-based methods have the opposite problem: it pays attention to multiple aspects of venture capitalists’ evaluation of the firm, including the entrepreneurial team, product or service, market, etc., but lacks a systematic financial assessment and cannot accurately quantify the firm’s value. Both of them is not accurate and comprehensive enough. Lastly, intellectual property valuation methods cannot deeply reflect the core value of intellectual property. This paper summarizes the commonly used methods of enterprise valuation and their limitations, which facilitates investors in choosing the optimal method in practical valuations.