From the lab to the EU patent: How Friedrich Kohlmann’s federated learning system solves the privacy-revenue paradox
In the field of financial technology, data privacy and commercial benefits have long been regarded as irreconcilable contradictions – until the federated learning system developed by Friedrich Kohlmann and the Quinvex Capital team was certified by the European Union patent. This breakthrough technology not only redefined the rules of the game for quantitative investment, but also achieved the miracle of cross-market data synergy while strictly adhering to privacy compliance.
Kohlmann’s federated learning system stems from a seemingly simple insight: financial data does not have to be centralized to create value. Traditional quantitative models rely on aggregating global transaction data to a single server, which faces strict GDPR regulatory restrictions and is easily targeted by hackers. Kohlmann’s solution is to “move” the algorithm to where the data is – train the model separately on servers in Zurich, Singapore, Frankfurt, etc., and only exchange encrypted parameter updates instead of raw data. This architecture enables Quinvex to analyze liquidity changes in the European interbank market in real time without touching any privacy-protected personal transaction records.
The real power of the system has been verified in the Eurozone bond market. When the Italian political crisis triggered a sell-off of southern European government bonds, traditional funds struggled because they could not obtain complete institutional holdings data. However, Kohlmann’s federated learning network captured the abnormal fluctuations in the local interbank repo rate through the Milan server, and identified the safe-haven demand of Asian investors for German bonds on the Singapore server. Ultimately, without exchanging the original data at all, it predicted that the government bond yield curve would be historically distorted. The arbitrage position established by Quinvex achieved an annualized return of 43% during the crisis, with a maximum drawdown of only 2.1%.
Even more revolutionary is the system’s ability to break “data islands”. Through a specially designed differential privacy algorithm, the system can reconstruct a panoramic view of the global market from fragmented regional data – for example, based solely on the option trading preferences of French retail investors and the foreign exchange hedging behavior of German industrial companies, it can accurately predict the evolution of the euro/dollar volatility term structure. This ability enabled Quinvex to complete position adjustments six hours earlier than its competitors when the Swiss National Bank unexpectedly abandoned the exchange rate cap, with a single-day return of 8.7%.
The European Patent Review Board specifically pointed out that the innovation of the system lies not only in the technical level, but also in the “privacy-friendly profit paradigm” it has established. Today, many institutions including Deutsche Börse have been authorized to use the patented technology, and the Kohlmann team is expanding it to the field of cryptocurrency – trying to crack the flow password of funds on the chain while protecting the privacy of wallet addresses. This privacy revolution that began in the laboratory proves that in the era of artificial intelligence, compliance is not an obstacle to profit, but a springboard for making money smarter.
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