-An achievable rate region R , G I( X, ; Y 1 X,, U), R 2 G 1(X2; YIX,, U), RI + R2 G 4x1, X2; Y), where P(U, xl, XZ, Y) = p(u)p(x,~u)p(x~~u)p(ylxl,x~), is established for the multiple-access channel with feedback. Time sharing of these achievable rates yields the rate region of this paper. This region generally exceeds the achievable rate region without feedback and exceeds the rate point found by Gaarder and Wolf for the binary erasure multiple-access channel with feedback. The presence of feedback allows the independent transmitters to understand each other's intended transmissions before the receiver has sufficient information to achieve the desired decoding. This allows the transmitters to cooperate in the transmission of information that resolves the residual uncertainty of the receiver. At the same time, independent information from the transmitters is superimposed on the cooperative correction information. The proof involves list codes and block Markov encoding.

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Real-world networks process structured connections since they have non-trivial vertex degree correlation and clustering. Here we propose a toy model of structure formation in real-world weighted network. In our model, a network evolves by topological growth as well as by weight change. In addition, we introduce the weighted assortativity coefficient, which generalizes the assortativity coefficient of a topological network, to measure the tendency of having a high-weighted link between two vertices of similar degrees. Network generated by our model exhibits scale-free behavior with a tunable exponent. Besides, a few non-trivial features found in real-world networks are reproduced by varying the parameter ruling the speed of weight evolution. Most importantly, by studying the weighted assortativity coefficient, we found that both topologically assortative and disassortative networks generated by our model are in fact weighted assortative.

In recent years, the enhanced sensing and computation capabilities of Internet of Things (IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile crowdsensing, a model owner announces a sensing task following which interested workers collect the required data. However, in some cases, a model owner may have insufficient data samples to build an effective machine learning model. To this end, we propose a Federated Learning based privacy preserving approach to facilitate collaborative machine learning among multiple model owners in mobile crowdsensing. Our system model allows collaborative machine learning without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, to ensure the stability of a federation through preventing free-riding attacks, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. Considering the inherent hierarchical structure of the involved entities, we propose a hierarchical incentive mechanism framework. Using the backward induction, we first solve the contract formulation and then proceed to solve the coalitional game with the merge and split algorithm. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design, in terms of incentive compatibility of our contract design and fair payoffs of model owners in stable federation formation.

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