Federated unlearning (FU) allows a participating client in a federated learning (FL) system to remove its contribution from the trained global model, thereby enforcing the client’s ``right to be forgotten'' (RTBF). However, from the perspective of a client that does not request unlearning, the activation of the FU process may disrupt ongoing FL training and introduce additional computational and time overhead. In such cases, a client opposed to unlearning may be incentivized to retaliate against the unlearning client(s). In this work, we take the first step toward demonstrating the feasibility of such retaliatory behavior by exploiting the information leakage introduced during the FU process. Specifically, we propose a novel unlearning-induced membership inference attack (MIA) model, followed by a coarse-to-fine data generation method that enables an adversarial client to locally reconstruct the unlearned data. Building on this reconstruction, we introduce two targeted retaliatory attacks: (1) Anti-Unlearning Attack (AUA), which hinders the global model from successfully forgetting the data intended for removal, and (2) Discrimination-Unlearning Attack (DUA), which specifically degrades the global model’s performance on the unlearned data. Extensive experiments across a variety of FU methods and settings validate the effectiveness of the proposed retaliatory attack framework.

We study a new online matching problem termed Online Capacitated General Matching with Knapsack (OCGMK), which generalizes the Online General Matching (OGM) problem. In the original OGM, vertices arrive sequentially and need to be paired with other vertices to maximize the total reward of pairing. Our study is the first to consider capacitated vertices in OGM: we allow each vertex to be assigned to multiple vertices up to a capacity limit. We also consider a previously unexamined knapsack constraint in OGM: assigning a pair of vertices has a cost, but the total cost is budgeted. To solve the OCGMK problem, we propose the Online Capacity-Knapsack Assignment (OCKA) algorithm, which constructs capacity-friendly sets and knapsack-friendly sets to simultaneously and effectively address both constraints. OCKA achieves a competitive ratio of ⍺=?/2?, where ?=1/(3+e^(-2)) and ? is the ratio between the overall cost of all edges and the cost budget. When the knapsack constraint is not imposed but the capacitated vertices remain, the competitive ratio of OCKA is ⍺'=1/2, recovering the previous best result for single-capacity OGM. We implement trace-driven experiments to evaluate the practical performance of OCKA on a real-world dating dataset, demonstrating the superior performance of OCKA in online dating applications.

Online data processing platforms offer free-tier services to attract users, where the service provider must strategically utilize limited resources to handle large amounts of low-value requests. Admission control has been leveraged to choose requests to process or skip, yet request uncertainties like unknown rewards and request numbers, and complicated interdependence among multi-resource consumption further pose challenges. To tackle these challenges, we propose a novel admission control solution, the Online Multi-resource Magician's Admission (OMMA) algorithm, that balances resource consumption with reward accumulation across online requests, while coordinating the intertwined consumption of different resources. OMMA is an online algorithm, with its performance evaluated by competitive ratio. OMMA attains a competitive ratio of C(1-K-1/2)M(1-L-1/2)N, where C is the constant capturing the intrinsic limitation of resource availability, K is the individual resource budget, M is the number of individual resources, L is the joint resource budget, and N is the number of joint resources. The competitive ratio achieved by OMMA is tight, meaning that it can be achieved by OMMA in certain problem instances. We implement trace-driven experiments to evaluate the practical performance of OMMA on a real-world LLM prompt dataset, demonstrating the superior performance of OMMA in online data processing services.