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Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist as clients' sensitive information can be inferred from intermediate computations. Moreover, such information leakage accumulates substantially over time as the same data is repeatedly used during the iterative learning process. As a result, it can be particularly difficult to balance the privacy-accuracy trade-off when designing privacy-preserving FL algorithms. This paper introduces Upcycled-FL, a simple yet effective strategy that applies first-order approximation at every even round of model update. Under this strategy, half of the FL updates incur no information leakage and require much less computational and transmission costs. We first conduct the theoretical analysis on the convergence (rate) of Upcycled-FL and then apply two perturbation mechanisms to preserve privacy. Extensive experiments on both synthetic and real-world data show that the Upcycled-FL strategy can be adapted to many existing FL frameworks and consistently improve the privacy-accuracy trade-off 

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. 

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. 

In this paper, we present a game-theoretic analysis of ransomware. To this end, we provide theoretical and empirical analysis of a two-player Attacker-Defender (A-D) game, as well as a Defender-Insurer (D-I) game; in the latter, the attacker is assumed to be a non-strategic third party. Our model assumes that the defender can invest in two types of protection against ransomware attacks: (1) general protection through a deterrence effort, making attacks less likely to succeed, and (2) a backup effort serving the purpose of recourse, allowing the defender to recover from successful attacks. The attacker then decides on a ransom amount in the event of a successful attack, with the defender choosing to pay ransom immediately, or to try to recover their data first while bearing a recovery cost for this recovery attempt. Note that recovery is not guaranteed to be successful, which may eventually lead to the defender paying the demanded ransom. Our analysis of the A-D game shows that the equilibrium falls into one of three scenarios: (1) the defender will pay the ransom immediately without having invested any effort in backup, (2) the defender will pay the ransom while leveraging backups as a credible threat to force a lower ransom demand, and (3) the defender will try to recover data, only paying the ransom when recovery fails. We observe that the backup effort will be entirely abandoned when recovery is too expensive, leading to the (worst-case) first scenario which rules out recovery. Furthermore, our analysis of the D-I game suggests that the introduction of insurance leads to moral hazard as expected, with the defender reducing their efforts; less obvious is the interesting observation that this reduction is mostly in their backup effort.