Financial services generate a huge volume of data that is extremely complex and varied.
These datasets are often stored in silos within organisations for various reasons, including …

This paper introduces Flamingo, a system for secure aggregation of data across a large set
of clients. In secure aggregation, a server sums up the private inputs of clients and obtains …

Federated Learning enables a population of clients, working with a trusted server, to
collaboratively learn a shared machine learning model while keeping each client's data …

In recent years, there has been a growing trend of applying reinforcement learning (RL) in
financial applications. This approach has shown great potential for decision-making tasks in …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …

Market simulation is an increasingly important method for evaluating and training trading
strategies and testing" what if" scenarios. The extent to which results from these simulations …

Simulated environments are increasingly used by trading firms and investment banks to
evaluate trading strategies before approaching real markets. Backtesting, a widely used …

Federated Learning is a technique that enables a large number of users to jointly learn a
shared machine learning model, managed by a centralized server, while the training data …

Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking
actions in the original problem environment or a simulated version of it. Breakthroughs in the …

Multi-agent market simulators usually require careful calibration to emulate real markets,
which includes the number and the type of agents. Poorly calibrated simulators can lead to …