We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data. Unlike the conventional FL framework that assumes the client's data is static, we consider scenarios where the clients' data distributions may be reshaped by the deployed decision model. In this work, we leverage the idea of distribution shift mappings in performative prediction to formalize this model-dependent data distribution shift and propose a performative FL framework. We first introduce necessary and sufficient conditions for the existence of a unique performative stable solution and characterize its distance to the performative optimal solution. Then we propose the performative FedAvg algorithm and show that it converges to the performative stable solution at a rate of O(1/T) under both full and partial participation schemes. In particular, we use novel proof techniques and show how the clients' heterogeneity influences the convergence. Numerical results validate our analysis and provide valuable insights into real-world applications.

Federated learning (FL) is a distributed learning framework that by design allows local edge devices to keep their training data. However, privacy leakage occurs through model updates and is a privacy protection concern that needs to be addressed. Over-the-air FL (OTA-FL) is a variant of FL designed for wireless edge networks by utilizing the inherent superposition property of the wireless medium. The wireless physical layer (PHY), in addition to providing resource and communication-efficient collaborative training via OTA-FL, can also be leveraged to enhance privacy for FL. This paper presents the PHY design to ensure differentially private (DP) OTA-FL. Specifically, by leveraging the Gaussian noise naturally present in the wireless channel, and deploying a dedicated artificial noise generator (cooperative jammer) when needed, a fully decentralized, dynamic power control strategy is proposed. This design relies on a resource-efficient FL framework with first-order approximation applied at every even iteration, thereby reducing the amount of information needed from clients. This approach can eliminate the need for artificial noise injection at the client side, typically required to achieve DP; the cooperative jammer is used for higher privacy requirement without transmission efficiency loss. The privacy analysis is provided via the Moments Accountant method, providing a tight privacy assessment. The convergence analysis is provided for non-convex learning objectives. Experiments conducted on real-world non-i.i.d. data demonstrate that our scheme outperforms the state-of-the-art method under the same DP requirement and illustrate the effectiveness of cooperative jammer in the case of stringent privacy requirements.

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness definitions to a user specified degree. To this end, we design an Integer Programming (IP) procedure that assigns abstention decisions for each training sample to satisfy a set of constraints. To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner. We analyze the feasibility of the IP procedure to determine the possible abstention rate for different levels of unfairness tolerance and accuracy constraint for achieving no harm. To the best of our knowledge, this work is the first to identify the theoretical relationships between the constraint parameters and the required abstention rate. Our theoretical results are important since a high abstention rate is often infeasible in practice due to a lack of human resources. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates.

In this paper we propose the use of large language models (LLMs) for characterizing, clustering, and fingerprinting raw text obtained from network measurements. To this end, We first train a transformer-based masked language model, namely RoBERTa, on a dataset containing hundreds of millions of banners obtained from Internet-wide scans. We further fine-tune this model using a contrastive loss function (driven by domain knowledge) to produce temporally stable numerical representations (embeddings) that can be used out-of-the-box for downstream learning tasks. Our embeddings are robust, resilient to small random changes in the content of a banner, and maintain proximity between embeddings of similar hardware/software products. We further cluster HTTP banners using a density-based approach (HDBSCAN), and examine the obtained clusters to generate text-based fingerprints for the purpose of labeling raw scan data. We compare our fingerprints to Recog, an existing database of manually curated fingerprints, and show that we can identify new IoT devices and server products that were not previously captured by Recog. Our proposed methodology poses an important direction for future research by utilizing state-of-the-art language models to automatically analyze, interpret, and label the large amounts of data generated by Internet scans.

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness as an online reinforcement learning problem for a policy affecting human populations. This formulation accommodates dynamical control objectives, such as achieving equitable population states, that cannot be incorporated into static formulations of fairness. We demonstrate that algorithmic solutions to the proposed fairness problem can adapt to unknown dynamics and, by sacrificing short-term incentives, drive the policy-population system towards more desirable equilibria. For the proposed setting, we develop an algorithm that adapts recent work in online learning and prove that this algorithm achieves simultaneous probabilistic bounds on cumulative loss and cumulative violations of fairness. In the classification setting subject to group fairness, we compare our proposed algorithm to several baselines, including the repeated retraining of myopic or distributionally robust classifiers, and to a deep reinforcement learning algorithm that lacks fairness guarantees. Our experiments model human populations according to evolutionary game theory and integrate real-world datasets.