TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

With the increased legislation around data privacy, federated learning (FL) has emerged as a promising technique that allows the clients (end-user) to collaboratively train deep learning (DL) models without transferring and storing the data in a centralized, third-party server. Despite the theoretical success, FL is yet to be adopted in real-world systems due to the hardware, computing, and various infrastructure constraints presented by the edge and mobile devices of the clients. As a result, simulated datasets, models, and experiments are heavily used by the FL research community to validate their theories and findings. We introduce TorchFL, a performant library for (i) bootstrapping the FL experiments, (ii) executing them using various hardware accelerators, (iii) profiling the performance, and (iv) logging the overall and agent-specific results on the go. Being built on a bottom-up design using PyTorch and Lightning, TorchFL provides ready-to-use abstractions for models, datasets, and FL algorithms, while allowing the developers to customize them as and when required.Comment: 20 pages, 15 figures, 4 table

Fairness And Feedback In Learning And Games

In this thesis, we study fairness and feedback effects in game theory and machine learning. In game theory and economics, financial or technological networks are analyzed for feedback effects. These studies analyze how the connectivity benefits or risk of contagious shocks affect the individual agents or the structure of the network formed by these rational agents. Towards this direction, in the first part of this thesis, we study a series of novel network formation games and analyze the structural properties of the equilibrium networks. Feedback effects can also occur in machine learning problems such as reinforcement learning or sequential allocation problems where the decisions of an algorithm over time can change the resources or actions available to the algorithm in the future as well as the environment in which the algorithm is operating. In the second part of this thesis, we study the effect of these feedback loops and ways to prevent them while also ensuring that the algorithm\u27s actions and allocations satisfy natural notions of fairness. In particular we are interested in quantifying the cost of imposing fairness on learning algorithms

Quantum Null Energy Condition and its (non)saturation in 2d CFTs

We consider the Quantum Null Energy Condition (QNEC) for holographic conformal field theories in two spacetime dimensions (CFT$_2$). We show that QNEC saturates for all states dual to vacuum solutions of AdS$_3$ Einstein gravity, including systems that are far from thermal equilibrium. If the Ryu-Takayanagi surface encounters bulk matter QNEC does not need to be saturated, whereby we give both analytical and numerical examples. In particular, for CFT$_2$ with a global quench dual to AdS$_3$-Vaidya geometries we find a curious half-saturation of QNEC for large entangling regions. We also address order one corrections from quantum backreactions of a scalar field in AdS$_3$ dual to a primary operator of dimension $h$ in a large central charge expansion and explicitly compute both, the backreacted Ryu--Takayanagi surface part and the bulk entanglement contribution to EE and QNEC. At leading order for small entangling regions the contribution from bulk EE exactly cancels the contribution from the back-reacted Ryu-Takayanagi surface, but at higher orders in the size of the region the contributions are almost equal while QNEC is not saturated. For a half-space entangling region we find that QNEC is gapped by $h/4$ in the large $h$ expansion.Comment: 37 pages, 9 figures; comments are welcom