684 research outputs found
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
Federated learning (FL) has emerged as a prevalent distributed machine
learning scheme that enables collaborative model training without aggregating
raw data. Cloud service providers further embrace Federated Learning as a
Service (FLaaS), allowing data analysts to execute their FL training pipelines
over differentially-protected data. Due to the intrinsic properties of
differential privacy, the enforced privacy level on data blocks can be viewed
as a privacy budget that requires careful scheduling to cater to diverse
training pipelines. Existing privacy budget scheduling studies prioritize
either efficiency or fairness individually. In this paper, we propose
DPBalance, a novel privacy budget scheduling mechanism that jointly optimizes
both efficiency and fairness. We first develop a comprehensive utility function
incorporating data analyst-level dominant shares and FL-specific performance
metrics. A sequential allocation mechanism is then designed using the Lagrange
multiplier method and effective greedy heuristics. We theoretically prove that
DPBalance satisfies Pareto Efficiency, Sharing Incentive, Envy-Freeness, and
Weak Strategy Proofness. We also theoretically prove the existence of a
fairness-efficiency tradeoff in privacy budgeting. Extensive experiments
demonstrate that DPBalance outperforms state-of-the-art solutions, achieving an
average efficiency improvement of , and an average
fairness improvement of .Comment: Accepted by IEEE International Conference on Computer Communications
(INFOCOM '24
Game Theory Relaunched
The game is on. Do you know how to play? Game theory sets out to explore what can be said about making decisions which go beyond accepting the rules of a game. Since 1942, a well elaborated mathematical apparatus has been developed to do so; but there is more. During the last three decades game theoretic reasoning has popped up in many other fields as well - from engineering to biology and psychology. New simulation tools and network analysis have made game theory omnipresent these days. This book collects recent research papers in game theory, which come from diverse scientific communities all across the world; they combine many different fields like economics, politics, history, engineering, mathematics, physics, and psychology. All of them have as a common denominator some method of game theory. Enjoy
Fair scheduling in cellular systems in the presence of noncooperative mobiles
We consider the problem of 'fair' scheduling the resources to one of the many mobile stations by a centrally controlled base station (BS). The BS is the only entity taking decisions in this framework based on truthful information from the mobiles on their radio channel. We study the well-known family of parametric -fair scheduling problems from a gametheoretic perspective in which some of the mobiles may be noncooperative. We first show that if the BS is unaware of the noncooperative behavior from the mobiles, the noncooperative mobiles become successful in snatching the resources from the other cooperative mobiles, resulting in unfair allocations. If the BS is aware of the noncooperative mobiles, a new game arises with BS as an additional player. It can then do better by neglecting the signals from the noncooperative mobiles. The BS, however, becomes successful in eliciting the truthful signals from the mobiles only when it uses additional information (signal statistics). This new policy along with the truthful signals from mobiles forms a Nash Equilibrium (NE) which we call a Truth Revealing Equilibrium. Finally, we propose new iterative algorithms to implement fair scheduling policies that robustify the otherwise non-robust (in presence of noncooperation) fair scheduling algorithms
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